Automated Residential Energy Audits And Savings Measurements Using A Smart Wifi Thermostat Enabled Data Mining Approach

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Automated Residential Energy Audits and Savings Measurements Using a Smart Wifi Thermostat Enabled Data Mining Approach

Author : Abdulrahman Mubarak Q. Alanezi
Publisher :
ISBN 13 :
Total Pages : 85 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Automated Residential Energy Audits and Savings Measurements Using a Smart Wifi Thermostat Enabled Data Mining Approach by : Abdulrahman Mubarak Q. Alanezi

Download or read book Automated Residential Energy Audits and Savings Measurements Using a Smart Wifi Thermostat Enabled Data Mining Approach written by Abdulrahman Mubarak Q. Alanezi and published by . This book was released on 2021 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: The building sector has been identified as one of the biggest contributions to electricity and natural gas consumption in the U.S. These findings have necessitated the need for the development of energy saving initiatives in the sector, which will aid in reducing greenhouse gas emission needed to reduce the risk of climate change. However, despite several efforts by state agencies, such as the implementation of Property Assessed Clean Energy (PACE) and On-Bill Repayment or On-Bill Financing of energy efficiency investments, there are significant challenges to achieving energy efficiency in the building sector. Fundamentally the question is "How do we find the most cost effective energy efficiency measures present in the world?" Conventional energy audits, the typical way to discern, struggle from high cost, inconsistency in audit recommendations, and a lack of people trained to deliver. Thus, the approach just is not capable of "at-scale" identification of the measures to address first, then second, and so on. Additionally, it is essential that the savings from any investment and/or even behavioral changes be capable of being measured with accuracy in order to improve the ability to find the most effective energy reduction measures existing in the broader building sector and in order to communicate the relative economic benefits from upgrades to building owners. At this time, unless there are short-interval energy meters in buildings, the ability to measure savings with accuracy is just not there. As a solution, this dissertation investigates utilizing smart Wi-Fi thermostats data to conduct visual energy audits and predict energy savings with improved accuracy from any energy systems upgrade and any behavioral modification. The study leverages data from 101 residences owned by the University of Dayton. In 2015 prior University of Dayton researchers completed energy audits of these; documenting the geometric and energy characteristics and occupancy, as well as documenting any unique energy consuming device such as washers/dyers/dishwashers in the residence. These houses provided a diversity of size, age, insulation, and energy effectiveness. Additionally, historical energy consumption data, as well as smart WiFi thermostat data with corresponding weather data, were collected for these houses. The archived thermostat measured temperature data was used to develop unique power spectrums for the measured interior temperature for each residence. The binned power spectral density is shown to be an effective signature of the energy effectiveness of the various energy characteristics associated with a residence. Moreover, the outdoor temperature for each meter period was binned into histogram groupings.This research utilizes an AutoML H2O package to determine the best machine learning algorithm for predicting both the energy characteristics and energy consumption, as well as complete the tuning needed to determine the best model hyperparameters. Machine learning models were trained to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER) using smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data. The models validation coefficient of performance (R2 values) were respectively 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-Values, furnace efficiency, and AC SEER. This research helped lift up the possibility of conducting low-cost, large-scale, data-based energy auditing of residences that rely only on data that could easily be collected for any residence.Similarly, a power spectrum derived from the measured thermostat indoor temperature is combined with outdoor temperature data and known residential geometrical and energy characteristics in order to train a singular machine learning model capable of predicting energy consumption in any residence. The best model obtained had a percentage mean absolute error (MAE) of 8.6% for predicting monthly gas consumption. This result indicates that the best model is effective to estimate energy savings from upgrades in residential buildings. Specifically, when it is applied to real residences in which attic insulation upgraded, the energy savings estimation uncertainty was less than 7%. This is a significant improvement over the ASHRAE recommended guidelines for estimating building energy consumptions and savings, which has been termed capable, at best, of resolving savings only greater than 10% of total consumption, and, in many cases, unable to resolve any savings at all.

Data Mining for Residential Buildings Using Smart Wifi Thermostats

Author : Kefan Huang
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Data Mining for Residential Buildings Using Smart Wifi Thermostats by : Kefan Huang

Download or read book Data Mining for Residential Buildings Using Smart Wifi Thermostats written by Kefan Huang and published by . This book was released on 2021 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart WiFi thermostats are not just a device for controlling heating and cooling comfort in buildings, they also can learn from occupant behaviors and permit occupants to control their comfort remotely. This research seeks to go beyond this state of the art by utilizing smart thermostat WiFi data from detached residences combined with outdoor condition data to develop dynamic models to predict room temperature and cooling/heating demand and then apply these models to new thermostat temperature scheduling scenarios, associated with lower energy cooling/heating. The ultimate objective of this effort is to reduce energy use in residences and demonstrate the ability to respond to peak utility demand events while maintaining thermal comfort within a minimally acceptable range. Back Propagation Neural Network (BPNN), Long-Short Term Memory (LSTM) and Encoder-Decoder LSTM approaches are used to develop these dynamic models. Results demonstrate that LSTM outperforms BPNN and Encoder-Decoder LSTM approach, yielding MAE value on testing data of less than 0.5oC, equal to the resolution error of the measured temperature and MAPE value on testing data of 0.64. Additionally, the models developed are shown to be highly accurate in predicting energy savings from aggressive vithermostat setpoint schedules aimed at yielding deep reduction (up to 14.3%) in heating and cooling energy, as well as energy reduction that cooling or heating could be curtailed in response to a high demand event while maintaining thermal comfort bands

Smart WI-FI Thermostat-enabled Thermal Comfort Control Saving for Any Residence Using Long-short Term Memory

Author : Abdulelah Alhamayani
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Smart WI-FI Thermostat-enabled Thermal Comfort Control Saving for Any Residence Using Long-short Term Memory by : Abdulelah Alhamayani

Download or read book Smart WI-FI Thermostat-enabled Thermal Comfort Control Saving for Any Residence Using Long-short Term Memory written by Abdulelah Alhamayani and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Indoor thermal comfort in residential buildings can not only represented by internal temperature; other factors can affect the thermal satisfaction, such as relative humidity and the Mean Radiant Temperature (MRT). Controlling the HVAC system based on those factors can be implemented these days due the smart technologies afforded. Prior research has explored automated control of thermal comfort based on the concept of a Predicted Mean Vote (PMV) index, which was developed to provide a model of perceived occupant's comfort. However, in previous studies the mean radiant temperature (MRT) was not estimated by adding the effect of the occupant's exposure to the solar irradiance. Research is posed to leverage prior work in automatically estimating the R-values of walls and ceilings using a combination of smart Wi-Fi thermostat, building geometry, and historical energy consumption to estimate the MRT with accuracy and thus provide a means to control for comfort, rather than temperature alone [43]. Further, a machine learning model of the indoor temperature based upon a Long-Short Term Memory Network is employed in order to assess the energy saving potential of comfort control for any residence. The model leveraged historical thermostat, weather, and solar data were used to dynamically predict the interior temperature and relative humidity. With a developed model, it is possible to simulate internal temperature, and thus always quantify the PMV value to maintain a reasonable comfort condition. Application of this thermal comfort control can yield an estimate for minimum cooling energy. The initial results showed cooling energy savings in excess 43%. Based on this research, it is proposed that the approach to control thermal comfort can be used to reduce cooling energy savings and a better representation of human comfort, with only having a smart Wi-Fi thermostat with readily available data.

Smart Thermostat Evaluation Protocol: December 2016 - May 2023

Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Smart Thermostat Evaluation Protocol: December 2016 - May 2023 by :

Download or read book Smart Thermostat Evaluation Protocol: December 2016 - May 2023 written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A smart thermostat is an internet-connected device that controls home heating, ventilation, and air-conditioning (HVAC) equipment and can automatically adjust temperature set points to optimize performance and achieve energy savings. Smart thermostat features often include two way communication, occupancy detection (such as geofencing and occupancy sensors), schedule learning, and seasonal optimization algorithms. Smart thermostats can control most conventional HVAC systems, including central air conditioners, heat pumps, and forced air furnaces. Several types of residential utility programs offer smart thermostats as replacements measures. Working with smart thermostat vendors, utilities can offer separate optimization programs to produce energy savings beyond those achieved by installing a smart thermostat. From an evaluation perspective, smart thermostat programs have several noteworthy features. First, the energy savings from a smart thermostat may change over the life of the device. As a smart thermostat is connected to the internet, original equipment manufacturers can update the thermostat software to improve the thermostat's energy efficiency. Likewise, users can adjust the thermostat settings and schedules over time in response to changes in weather, thermal comfort, energy prices, or preferences for energy efficiency. Additionally, many thermostat manufacturers offer seasonal optimization programs that recommend changes or make minor, automated adjustments to the thermostat settings to improve energy efficiency. These opt-in programs are now standard offerings for many smart thermostat manufacturers and provided at no additional cost to users. The potential for software updates and continuous optimization and the evolving nature of user interactions mean future energy savings may differ from first-year savings and the energy savings of smart thermostats may need to be evaluated more than once. Second, smart thermostats often have small unit energy savings relative to a home's total energy consumption, especially in comparison to whole- home retrofit programs. This can make it difficult to detect the smart thermostat savings in billing or advanced metering infrastructure (AMI) meter consumption data. For example, as cooling loads in many regions average about 20% of annual electricity consumption, smart thermostat savings of 10% of cooling energy use would equate to a 2% reduction in home electricity consumption. Evaluators should use regression analysis of whole-home billing consumption or advanced metering infrastructure (AMI) meter consumption data to evaluate smart thermostat savings because, as explained at greater length below , these data are usually available to evaluators and regression can control for the impacts of weather and other potentially confounding factors on a home's energy consumption. Finally, as with other energy efficiency programs, participation in smart thermostat programs is self-selective. As discussed at greater length below , smart thermostat participants tend to be, among other things, younger, higher-income, and more likely to adopt electric vehicles (EVs) and internet connected devices than nonparticipants. These differences are often unobservable to the evaluator and correlated with a home's energy consumption, creating the potential for bias in estimating savings. Due to the small unit savings of thermostats, errors and biases from self-selection that may not be very consequential when evaluating a whole- home retrofits (e.g., +/-2% of home electricity consumption) can have a major impact when evaluating the savings and cost-effectiveness of smart thermostat programs. A percentage point change in the estimated savings could affect the cost-effectiveness of a program. This means it is important for evaluators to assess and to minimize the potential for error from selection bias in estimating smart thermostat program savings. The Uniform Methods Project provides model protocols for determining energy savings and demand reductions that result from specific energy efficiency measures implemented through state and utility programs. In most cases, the measure protocols are based on a particular option identified by the International Performance Verification and Measurement Protocol; however, this work provides a more detailed approach to implementing that option. Each chapter is written by technical experts in collaboration with their peers, reviewed by industry experts, and subject to public review and comment. The UMP protocols can be used by utilities, program administrators, public utility commissions, evaluators, and other stakeholders for both program planning and evaluation.

Residential Energy Auditing and Improvement

Author : Stan Harbuck
Publisher : CRC Press
ISBN 13 : 8770223165
Total Pages : 662 pages
Book Rating : 4.7/5 (72 download)

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Book Synopsis Residential Energy Auditing and Improvement by : Stan Harbuck

Download or read book Residential Energy Auditing and Improvement written by Stan Harbuck and published by CRC Press. This book was released on 2021-01-07 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is for energy auditors or retrofitters, whether they work in the weatherization program or in the private arena, and is intended to help them prepare for several certifications. These include programs with BPI, RESNET-HERS, DOE/NREL, and AEE (Association of Energy Engineers). The material in this book contains industry procedures and techniques and is intended to be an educational resource. Topics covered include the house as a system, the auditor’s tools, weatherization, sealants, insulation and barriers, retrofitting, heating and cooling, baseload, and new construction. A number of additional appendices are included to provide the reader with valuable information in the performance of a residential energy audit.

The Residential Energy Audit Manual

Author : United States. Department of Energy
Publisher :
ISBN 13 :
Total Pages : 496 pages
Book Rating : 4.3/5 (121 download)

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Book Synopsis The Residential Energy Audit Manual by : United States. Department of Energy

Download or read book The Residential Energy Audit Manual written by United States. Department of Energy and published by . This book was released on 1981 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Mining for Accurately Estimating Residential Natural Gas Energy Consumption and Savings Using a Random Forest Approach

Author : Adel Ali Naji
Publisher :
ISBN 13 :
Total Pages : 60 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Data Mining for Accurately Estimating Residential Natural Gas Energy Consumption and Savings Using a Random Forest Approach by : Adel Ali Naji

Download or read book Data Mining for Accurately Estimating Residential Natural Gas Energy Consumption and Savings Using a Random Forest Approach written by Adel Ali Naji and published by . This book was released on 2019 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cost effective energy efficiency improvements in residential buildings could yield annual electricity savings of approximately 30 percent within this sector for the United States. Furthermore, such investment can create millions of direct and indirect jobs throughout the economy. Unfortunately, realizing these savings is difficult. One of the impediments for realization is the means by which savings can be estimated. The prevalent approach is to use energy models to estimate. However, actual energy savings are more often than not over-predicted by energy models, leading to wariness on the part of potential investors which include the residents themselves. A driver for this research is 500 residential buildings with known geometrical and historical energy data owned by the University of Dayton. Further, the energy characteristics of these buildings are knowable. This housing stock offers significant diversity in size (ranging from a floor area of 715 to 2800 square feet), age (from the early 1900s to new construction) and energy effectiveness, the latter occurring as a result of gradual improvements made to residences over the past 15 years. In the summer of 2015 energy and building data audits were conducted on a subset of 139 homes. The audit documented the areas of the walls and attic, the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system specifications domestic hot water equipment specifications, interior attic penetration area, and the presence of a basement. A data mining approach was used for developing the Random Forest (RF) model to predict energy consumption in a group of single family houses based upon knowledge of residential energy characteristics, historical energy consumption, occupancy and building geometrical data, as well as inferred energy characteristics from energy consumption data. The model was used to estimate savings and develop a cost implementation model from discrete measures for each residence. Thus, the cost effectiveness of each possible measure could be assessed. From these, prioritized energy reduction measures among all possible measures for all residences could be identified based upon a "worst-to-first" strategy in order to achieve community-scale energy (and carbon) savings most cost effectively. The results when extrapolated 45,000 single family houses in Dayton, Ohio show that a preliminary investment in energy efficiency of $26 million can achieve annual energy cost savings of $2.21M per year. As or more importantly, an Economic Input-Output analysis reveals a total sequential economic impact of $41.2M from the investment. Thus, this approach offers significant and indisputable local impact.

Data Analysis in Energy

Author : Qiancheng Sun
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

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Book Synopsis Data Analysis in Energy by : Qiancheng Sun

Download or read book Data Analysis in Energy written by Qiancheng Sun and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For residences energy consumption analysis, using physics-base modeling with different input conditions (e.g., residences area, window size, insulation materials, etc.) is the main way to analyze energy consumption. In many studies, researchers employ Energy Plus or 1-D or multi-dimensional heat transfer models to predict residential energy consumption, etc... However, such methods require significant on-site data collection and yield predictions with substantial uncertainty. Additionally, these approaches have been shown to be highly influenced by the practitioner. Thus, the predictions from one practitioner to another have wide variation. Another way to model and analyze residential energy is based upon data-base modeling. Compared with the traditional physic-base modeling, data-base modeling requires less input data (although they could benefit from even more data) to yield much more accurate energy consumption prediction, because in effect, the predictions are 'calibrated' against actual data. Two applications of data-base modeling are presented in this dissertation: namely: 1). Reliance upon smart Wi-Fi thermostat data to predict the solar heat gain of a residence; and 2). Reliance on millions of residential buildings to develop automated energy pre- screening audits. 3 The first application is relevant to any residence in which a smart Wi-Fi thermostat exists. The second demonstrates the value of statewide, regional, and even national energy and building databases to prioritize energy reduction investments in society.

A Smart Wifi Thermostat Data-based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant Temperature

Download A Smart Wifi Thermostat Data-based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant Temperature PDF Online Free

Author : Yisheng Lou
Publisher :
ISBN 13 :
Total Pages : 79 pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis A Smart Wifi Thermostat Data-based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant Temperature by : Yisheng Lou

Download or read book A Smart Wifi Thermostat Data-based Neural Network Model for Controlling Thermal Comfort in Residences Through Estimates of Mean Radiant Temperature written by Yisheng Lou and published by . This book was released on 2021 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Indoor thermal comfort in residential buildings is usually achieved by tenants manually adjusting fixed temperature set-points; this is known as a 'static' method. Prior research has explored automated control of thermal comfort based on the concept of a Predicted Mean Vote (PMV) index, which has been developed to provide a model of perceived human comfort. However, one of the dominant contributions to this index, the Mean Radiant Temperature (MRT), effectively the mean radiant temperature of the surrounding interior surfaces, has either been: 1) inaccurately assumed to be the same as indoor air temperature; and/or 2) costly to implement due to the need for numerous additional sensors. Research is posed to leverage prior work in automatically estimating the R-values of walls and ceilings using a combination of smart WiFi thermostat, building geometry, and historical energy consumption [51] to estimate the MRT with accuracy and thus provide a means to control for comfort, rather than temperature alone. In order to assess the energy saving potential of comfort control for any residence, a machine learning model of the indoor temperature based upon a NARX Neural Network is employed. This model leverages historical thermostat and weather data to develop a means to dynamically predict the interior temperature. With a developed model, it is possible to simulate different temperature set-points on indoor temperature, and thus identify the optimal set-point temperature at all times needed to maintain a reasonable comfort condition. Application of this ideal temperature set-point for minimum human comfort to historical weather data and indoor weather conditions can yield an estimate for minimum cooling energy. The initial results showed cooling energy savings in excess of 83% and 95%, respectively, for high- and low-efficiency residences. Based on this research, it is proposed that the approach to estimate MRT can be used to calculate a more accurate PMV value and a better representation of human comfort, without anything more than a smart WiFi thermostat with readily available data. Thus, a control strategy based on this paradigm can both achieve thermal comfort in residential buildings and less energy consumption. In addition, a Model Predictive Controller (MPC) is developed to realize more realistic and sensible control. Compressor protection is also considered in the development of the controller.

Predicting Residential Heating Energy Consumption and Savings Using Neural Network Approach

Author : Badr Ibrahim Al Tarhuni
Publisher :
ISBN 13 :
Total Pages : 61 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Predicting Residential Heating Energy Consumption and Savings Using Neural Network Approach by : Badr Ibrahim Al Tarhuni

Download or read book Predicting Residential Heating Energy Consumption and Savings Using Neural Network Approach written by Badr Ibrahim Al Tarhuni and published by . This book was released on 2019 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrading and replacing inefficient energy-consuming equipment in both the residential and commercial building sectors offers a great investment opportunity, with significant impacts on economic, climate, and employment. Cost effective retrofits of residential buildings could yield annual electricity savings of approximately 30 percent in the United States. This obviously could reduce greenhouse gas emissions in the U.S. significantly. Further, investment in energy efficiency can create millions direct and indirect jobs throughout the economy for manufacturers and service providers that supply the building industry. Unfortunately, the prediction in savings, which has been generally based upon energy models, has been circumspect, with energy savings typically over-predicted. Investor confidence as a result can degrade. An enabler for this research is a collective grouping of over 500 residential buildings used for student housing owned by a Midwestern U.S. university. These residences offer significant variation in size, ranging from a floor area of 715 to 2800 square feet, in age, ranging from the early 1900s to new construction, and energy effectiveness, the latter occurring mostly as a result of improvements made gradually over time to some residences over the past fifteen years. The historical monthly natural gas and electricity energy consumption for these houses is available. Additionally, in the summer of 2015, energy and building data audits were completed on a total of 139 residences. Documented in these audits were the amount and type of insulation in the walls and attic, areas and types of windows, floor heights, maximum occupancy, appliance (refrigerator, range, oven) specifications, heating ventilation air-conditioning system specifications, domestic hot water equipment specifications, and the presence of a basement. Finally, county auditor real estate information was relied upon to obtain detailed features of each residence, including the age of the house, number of floors, floor area of each level, and total floor area. Using this data, a data mining approach based upon an artificial neural network (ANN) model was shown to be effective in estimating the annual heating energy savings from a variety of measures for a large number of houses for which energy characteristics are known and energy consumption data is available. In combination with cost models for implementation of the measures, the cost effectiveness of every measure considered for each residence was estimable. This preliminary study provides the starting point for the research presented here. With knowledge of the individual cost effectiveness of all measures within a collective grouping of residences, it becomes possible to adopt a strategy for energy reduction based upon a "worst to first" methodology. The economic impact of adoption of this methodology is then determined using an economic-input-output (EIO) approach. Considering only those measures that are economically viable and extrapolating the results from this study to the entire Dayton region yields with the initial energy efficiency investment of $26.1M can result in a total local economic impact of $41.2M (i.e. summation of direct, indirect, and induced) and additional economic impacts stemming from the annual energy savings of $2.21M for the lifetime of the considered EE measures.

The Residential Energy Audit Manual

Author : Dale Schueman
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 520 pages
Book Rating : 4.X/5 (2 download)

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Book Synopsis The Residential Energy Audit Manual by : Dale Schueman

Download or read book The Residential Energy Audit Manual written by Dale Schueman and published by Prentice Hall. This book was released on 1992 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: This fully updated edition is a guide for techniques and guidelines on implementing a residential energy audit programme. Step by step the manual shows how to perform an energy audit of the home, offering authoritative advice from energy specialists.

Insights from Smart Meters. Ramp-up, Dependability, and Short-term Persistence of Savings from Home Energy Reports

Author :
Publisher :
ISBN 13 :
Total Pages : 32 pages
Book Rating : 4.:/5 (951 download)

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Book Synopsis Insights from Smart Meters. Ramp-up, Dependability, and Short-term Persistence of Savings from Home Energy Reports by :

Download or read book Insights from Smart Meters. Ramp-up, Dependability, and Short-term Persistence of Savings from Home Energy Reports written by and published by . This book was released on 2015 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smart meters, smart thermostats, and other new technologies provide previously unavailable high-frequency and location-specific energy usage data. Many utilities are now able to capture real-time, customer specific hourly interval usage data for a large proportion of their residential and small commercial customers. These vast, constantly growing streams of rich data (or, "big data") have the potential to provide novel insights into key policy questions about how people make energy decisions. The richness and granularity of these data enable many types of creative and cutting-edge analytics. Technically sophisticated and rigorous statistical techniques can be used to pull useful insights out of this high-frequency, human-focused data. In this series, we call this "behavior analytics." This kind of analytics has the potential to provide tremendous value to a wide range of energy programs. For example, disaggregated and heterogeneous information about actual energy use allows energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; enables evaluation, measurement and verification (EM & V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and may provide better insights into the energy and peak hour savings associated with EE and DR programs (e.g., behavior-based (BB) programs). The goal of this series is to enable evidence-based and data-driven decision making by policy makers and industry stakeholders, including program planners, program administrators, utilities, state regulatory agencies, and evaluators. We focus on research findings that are immediately relevant.

Automated Diagnostics and Analytics for Buildings

Author : Barney L. Capehart
Publisher : CRC Press
ISBN 13 : 8770223211
Total Pages : 640 pages
Book Rating : 4.7/5 (72 download)

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Book Synopsis Automated Diagnostics and Analytics for Buildings by : Barney L. Capehart

Download or read book Automated Diagnostics and Analytics for Buildings written by Barney L. Capehart and published by CRC Press. This book was released on 2021-01-07 with total page 640 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the widespread availability of high-speed, high-capacity microprocessors and microcomputers with high-speed communication ability, and sophisticated energy analytics software, the technology to support deployment of automated diagnostics is now available, and the opportunity to apply automated fault detection and diagnostics to every system and piece of equipment in a facility, as well as for whole buildings, is imminent. The purpose of this book is to share information with a broad audience on the state of automated fault detection and diagnostics for buildings applications, the benefits of those applications, emerging diagnostic technology, examples of field deployments, the relationship to codes and standards, automated diagnostic tools presently available, guidance on how to use automated diagnostics, and related issues.

Wellness Protocol for Smart Homes

Author : Hemant Ghayvat
Publisher : Springer
ISBN 13 : 3319520482
Total Pages : 165 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Wellness Protocol for Smart Homes by : Hemant Ghayvat

Download or read book Wellness Protocol for Smart Homes written by Hemant Ghayvat and published by Springer. This book was released on 2017-01-05 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the development of wellness protocols for smart home monitoring, aiming to forecast the wellness of individuals living in ambient assisted living (AAL) environments. It describes in detail the design and implementation of heterogeneous wireless sensors and networks as applied to data mining and machine learning, which the protocols are based on. Further, it shows how these sensor and actuator nodes are deployed in the home environment, generating real-time data on object usage and other movements inside the home, and therefore demonstrates that the protocols have proven to offer a reliable, efficient, flexible, and economical solution for smart home systems. Documenting the approach from sensor to decision making and information generation, the book addresses various issues concerning interference mitigation, errors, security and large data handling. As such, it offers a valuable resource for researchers, students and practitioners interested in interdisciplinary studies at the intersection of wireless sensing processing, radio communication, the Internet of Things and machine learning, and in how they can be applied to smart home monitoring and assisted living environments.

Baseline Energy Modeling Approach for Residential Measurement and Verification Applications

Author : Eliot Crowe
Publisher :
ISBN 13 :
Total Pages : 48 pages
Book Rating : 4.:/5 (92 download)

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Book Synopsis Baseline Energy Modeling Approach for Residential Measurement and Verification Applications by : Eliot Crowe

Download or read book Baseline Energy Modeling Approach for Residential Measurement and Verification Applications written by Eliot Crowe and published by . This book was released on 2015 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The growing availability of electric interval data from smart meters is driving development of consumer-facing analytical software that has the potential to offer automated measurement and verification (M&V) of savings from energy efficiency projects. This capability would present new opportunities to utilities including supporting financial transactions based on measured energy savings, or allowing for a greater variety of program measures. This report provides the results of analytical research on establishing a method for developing a robust energy baseline regression model and evaluation of how the model performed on the electric interval dataset from NEEA's Residential Building Stock Analysis metering study. The report presents results of a literature review and a high-level review of home energy management system (HEMS) M&V capabilities. The results of the analytical research provide a strong foundation for future efforts toward an automated M&V approach using interval data, while the literature review confirmed the team's initial belief that utilities have not yet applied M&V approaches for residential applications using interval data. Additionally, the HEMS research uncovered no instances of products performing utility program M&V"--Publisher's description (viewed Sept. 4, 2015).

Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance

Author : Zhun Yu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (13 download)

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Book Synopsis Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance by : Zhun Yu

Download or read book Mining Hidden Knowledge from Measured Data for Improving Building Energy Performance written by Zhun Yu and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Analysis, Modeling and Optimization of Residential Energy Use from Smart Meter Data

Author : Krystian Xavier Perez
Publisher :
ISBN 13 :
Total Pages : 352 pages
Book Rating : 4.:/5 (982 download)

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Book Synopsis Analysis, Modeling and Optimization of Residential Energy Use from Smart Meter Data by : Krystian Xavier Perez

Download or read book Analysis, Modeling and Optimization of Residential Energy Use from Smart Meter Data written by Krystian Xavier Perez and published by . This book was released on 2016 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Approximately 38% of electricity consumption within the United States can be attributed to residential buildings, a vast share of which is in heating, ventilation and cooling. The load placed on the grid by residential consumers is highly variable and strongly influenced by weather and human activity patterns. Meeting fluctuations in demand is challenging and expensive for electricity producers and grid operators. Reducing variability in residential energy use can contribute significantly to increasing the uniformity of energy demand on the grid and diminish reliance on inefficient, polluting “peaking” plants that are used to meet extremely high demands. Achieving this goal requires tight coordination between energy consumption and generation, as well as the means to store energy generated in periods of low demand for use during the time intervals when consumer demand peaks. There is a common perception that a single home has a minor impact on the entire grid. However, owing to the fact that consumption patterns of homes are similar, while a single home does not have a large impact on the grid, entire neighborhoods do. Motivated by the above, this work explores the interaction between residential energy consumption and the electric grid. An analysis, modeling and optimization framework on smart meter data is developed to anticipate and modulate energy usage of ensembles of residential homes in order to reduce peak power demand. Much of the data used in this work come from Pecan Street, Inc., a smart grid demonstration project in Austin, TX. First, a nonintrusive load monitoring algorithm is developed to isolate air-conditioning (A/C) energy use from whole-house energy consumption data. Subsequently, a simplified reduced-order model is derived from smart meter data and thermostat set-point data to predict A/C energy use. The models of an ensemble of homes are placed within a centralized model predictive control scheme to minimize peak community A/C energy use. Reductions in peak energy use are achieved by shifting the thermostat set-points of individual homes. The approach is further expanded by simultaneously scheduling the operation of time-shiftable appliances to further reduce the community peak load. This integrated operation reduces peak loads by an average of 25.5%. This work also considers the impact of control and optimization techniques on designing a micro-grid that operates near autonomously from the electric power grid. Lastly, this work presents a tool to compare energy demand patterns of houses from smart meter data and indicates that high-energy houses would benefit from energy audits to improve energy efficiency.