Data Driven Models Applied In Building Load Forecasting For Residential And Commercial Buildings

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Data Driven Models Applied in Building Load Forecasting for Residential and Commercial Buildings

Author : SM Mahbobur Rahman
Publisher :
ISBN 13 : 9781339034744
Total Pages : 98 pages
Book Rating : 4.0/5 (347 download)

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Book Synopsis Data Driven Models Applied in Building Load Forecasting for Residential and Commercial Buildings by : SM Mahbobur Rahman

Download or read book Data Driven Models Applied in Building Load Forecasting for Residential and Commercial Buildings written by SM Mahbobur Rahman and published by . This book was released on 2015 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: A significant portion of the operating costs of utilities comes from energy production. Machine learning methods are widely used for short-term load forecasts for commercial buildings and also the utility grid. These forecasts are used to minimize unit power production costs for the energy managers for better planning of power units and load management. In this work, three different state-of-art machine learning methods i.e. Artificial Neural Network, Support Vector Regression and Gaussian Process Regression are applied in hour ahead and 24 –hour ahead building energy forecasting. The work uses four residential buildings and one commercial building located in Downtown, San Antonio as test-bed using energy consumption data from those buildings monitored in real-time. Uncertainty quantification analysis is conducted to understand the confidence in each forecast using Bayesian Network. Using a combination of weather variables and historical load, forecasting is done in a supervised way based on a moving window training algorithm. A range of comparisons between different forecasting models in terms of relative accuracy are then presented.

Data-Driven Modelling of Non-Domestic Buildings Energy Performance

Author : Saleh Seyedzadeh
Publisher : Springer Nature
ISBN 13 : 303064751X
Total Pages : 161 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Data-Driven Modelling of Non-Domestic Buildings Energy Performance by : Saleh Seyedzadeh

Download or read book Data-Driven Modelling of Non-Domestic Buildings Energy Performance written by Saleh Seyedzadeh and published by Springer Nature. This book was released on 2021-01-15 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book outlines the data-driven modelling of building energy performance to support retrofit decision-making. It explains how to determine the appropriate machine learning (ML) model, explores the selection and expansion of a reasonable dataset and discusses the extraction of relevant features and maximisation of model accuracy. This book develops a framework for the quick selection of a ML model based on the data and application. It also proposes a method for optimising ML models for forecasting buildings energy loads by employing multi-objective optimisation with evolutionary algorithms. The book then develops an energy performance prediction model for non-domestic buildings using ML techniques, as well as utilising a case study to lay out the process of model development. Finally, the book outlines a framework to choose suitable artificial intelligence methods for modelling building energy performances. This book is of use to both academics and practising energy engineers, as it provides theoretical and practical advice relating to data-driven modelling for energy retrofitting of non-domestic buildings.

Data-driven Analytics for Sustainable Buildings and Cities

Author : Xingxing Zhang
Publisher : Springer Nature
ISBN 13 : 9811627789
Total Pages : 450 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Data-driven Analytics for Sustainable Buildings and Cities by : Xingxing Zhang

Download or read book Data-driven Analytics for Sustainable Buildings and Cities written by Xingxing Zhang and published by Springer Nature. This book was released on 2021-09-11 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the interdisciplinary and transdisciplinary fields of energy systems, occupant behavior, thermal comfort, air quality and economic modelling across levels of building, communities and cities, through various data analytical approaches. It highlights the complex interplay of heating/cooling, ventilation and power systems in different processes, such as design, renovation and operation, for buildings, communities and cities. Methods from classical statistics, machine learning and artificial intelligence are applied into analyses for different building/urban components and systems. Knowledge from this book assists to accelerate sustainability of the society, which would contribute to a prospective improvement through data analysis in the liveability of both built and urban environment. This book targets a broad readership with specific experience and knowledge in data analysis, energy system, built environment and urban planning. As such, it appeals to researchers, graduate students, data scientists, engineers, consultants, urban scientists, investors and policymakers, with interests in energy flexibility, building/city resilience and climate neutrality.

Data-driven Whole Building Energy Forecasting Model for Data Predictive Control

Author : Liang Zhang
Publisher :
ISBN 13 :
Total Pages : 340 pages
Book Rating : 4.:/5 (112 download)

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Book Synopsis Data-driven Whole Building Energy Forecasting Model for Data Predictive Control by : Liang Zhang

Download or read book Data-driven Whole Building Energy Forecasting Model for Data Predictive Control written by Liang Zhang and published by . This book was released on 2018 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the United States, the buildings sector accounted for about 41% of primary energy consumption. Building control and operation strategies have a great impact on building energy efficiency and the development of building-grid integration. Model predictive control (MPC) has received extensive attention from researchers in the field of whole building control and operation strategies. To develop MPC for whole building control and operation, high-fidelity building energy forecasting model is one of the most critical components. Data-driven energy forecasting model is typically developed using statistical methods to capture the relationship between building energy consumption and collected building data, such as operation data. MPC built with a data-driven model is also termed as data predictive control (DPC). Due to the surge of machine learning and the advances of building automation system (BAS), data-driven energy forecasting model and DPC for building control are increasingly studied in academia and applied in industry. However, three gaps impede the development of high-fidelity and cost-effective data-driven building energy forecasting models and predictive control strategies: Gap 1: Active learning, the key to defy data bias in building operation data, is hardly studied and applied to the area of data-driven building energy forecasting modeling; Gap 2: Feature selection to defy high data dimensionality is widely applied to building energy modeling process but there lacks a systematic and scalable methodology; Gap 3: Active learning and feature selection have not been systematically integrated for whole building DPC application. In this dissertation, to address the three gaps mentioned above, three research objectives are proposed: Objective 1: Develop active learning strategies in the application of data-driven building energy forecasting modeling to defy data bias; Objective 2: Develop a systematic feature selection procedure in the application of data-driven building energy forecasting modeling to defy high data dimensionality; Objective 3: Develop an integrated active learning and feature selection framework for data-driven building energy forecasting modeling used for whole building DPC application. In this thesis, the integrated framework of active learning and feature selection is developed to improve the performance of data-driven building energy forecasting modeling that can be used for future DPC applications. The framework provides a systematic methodology and automatic workflow that starts with collecting raw data from BAS to the establishment of data-driven energy models and DPC controllers. The developed strategies and framework are evaluated in a number of virtual and real building testbeds. Improved performance is observed from the building energy forecasting models built using the developed active learning strategy, systematic feature selection procedure, and integrated framework of active learning and feature selection, respectively. A DPC controller is also developed using an energy forecasting model built with the developed framework. Using virtual testbeds, the developed DPC controller is demonstrated to have better performance, in terms of total electricity cost, peak load shifting capability, and average CPU time, which further shows the effectiveness of the developed framework.

Building Performance Simulation for Design and Operation

Author : Jan L.M. Hensen
Publisher : Routledge
ISBN 13 : 0429688539
Total Pages : 958 pages
Book Rating : 4.4/5 (296 download)

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Book Synopsis Building Performance Simulation for Design and Operation by : Jan L.M. Hensen

Download or read book Building Performance Simulation for Design and Operation written by Jan L.M. Hensen and published by Routledge. This book was released on 2019-04-24 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: When used appropriately, building performance simulation has the potential to reduce the environmental impact of the built environment, to improve indoor quality and productivity, as well as to facilitate future innovation and technological progress in construction. Since publication of the first edition of Building Performance Simulation for Design and Operation, the discussion has shifted from a focus on software features to a new agenda, which centres on the effectiveness of building performance simulation in building life cycle processes. This new edition provides a unique and comprehensive overview of building performance simulation for the complete building life cycle from conception to demolition, and from a single building to district level. It contains new chapters on building information modelling, occupant behaviour modelling, urban physics modelling, urban building energy modelling and renewable energy systems modelling. This new edition keeps the same chapter structure throughout including learning objectives, chapter summaries and assignments. Moreover, the book: • Provides unique insights into the techniques of building performance modelling and simulation and their application to performance-based design and operation of buildings and the systems which service them. • Provides readers with the essential concepts of computational support of performance-based design and operation. • Provides examples of how to use building simulation techniques for practical design, management and operation, their limitations and future direction. It is primarily intended for building and systems designers and operators, and postgraduate architectural, environmental or mechanical engineering students.

Building Energy Modeling

Author : Can Cui
Publisher :
ISBN 13 :
Total Pages : 152 pages
Book Rating : 4.:/5 (953 download)

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Book Synopsis Building Energy Modeling by : Can Cui

Download or read book Building Energy Modeling written by Can Cui and published by . This book was released on 2016 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings consume nearly 50% of the total energy in the United States, which drives the need to develop high-fidelity models for building energy systems. Extensive methods and techniques have been developed, studied, and applied to building energy simulation and forecasting, while most of work have focused on developing dedicated modeling approach for generic buildings. In this study, an integrated computationally efficient and high-fidelity building energy modeling framework is proposed, with the concentration on developing a generalized modeling approach for various types of buildings. First, a number of data-driven simulation models are reviewed and assessed on various types of computationally expensive simulation problems. Motivated by the conclusion that no model outperforms others if amortized over diverse problems, a meta-learning based recommendation system for data-driven simulation modeling is proposed. To test the feasibility of the proposed framework on the building energy system, an extended application of the recommendation system for short-term building energy forecasting is deployed on various buildings. Finally, Kalman filter-based data fusion technique is incorporated into the building recommendation system for on-line energy forecasting. Data fusion enables model calibration to update the state estimation in real-time, which filters out the noise and renders more accurate energy forecast. The framework is composed of two modules: off-line model recommendation module and on-line model calibration module. Specifically, the off-line model recommendation module includes 6 widely used data-driven simulation models, which are ranked by meta-learning recommendation system for off-line energy modeling on a given building scenario. Only a selective set of building physical and operational characteristic features is needed to complete the recommendation task. The on-line calibration module effectively addresses system uncertainties, where data fusion on off-line model is applied based on system identification and Kalman filtering methods. The developed data-driven modeling framework is validated on various genres of buildings, and the experimental results demonstrate desired performance on building energy forecasting in terms of accuracy and computational efficiency. The framework could be easily implemented into building energy model predictive control (MPC), demand response (DR) analysis and real-time operation decision support systems.

Advanced Models of Energy Forecasting

Author : Xun Zhang
Publisher : Frontiers Media SA
ISBN 13 : 283250681X
Total Pages : 200 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Advanced Models of Energy Forecasting by : Xun Zhang

Download or read book Advanced Models of Energy Forecasting written by Xun Zhang and published by Frontiers Media SA. This book was released on 2022-11-23 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Short-Term Load Forecasting 2019

Author : Antonio Gabaldón
Publisher : MDPI
ISBN 13 : 303943442X
Total Pages : 324 pages
Book Rating : 4.0/5 (394 download)

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Book Synopsis Short-Term Load Forecasting 2019 by : Antonio Gabaldón

Download or read book Short-Term Load Forecasting 2019 written by Antonio Gabaldón and published by MDPI. This book was released on 2021-02-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.

15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022

Author : R. A. Aliev
Publisher : Springer Nature
ISBN 13 : 3031252527
Total Pages : 777 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis 15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022 by : R. A. Aliev

Download or read book 15th International Conference on Applications of Fuzzy Systems, Soft Computing and Artificial Intelligence Tools – ICAFS-2022 written by R. A. Aliev and published by Springer Nature. This book was released on 2023-02-28 with total page 777 pages. Available in PDF, EPUB and Kindle. Book excerpt: The general scope of the book covers diverse areas of fuzzy systems, soft computing, AI tools such as uncertain computation, decision-making under imperfect information, deep learning, and others. The topics of the papers include theory and application of Soft Computing, Neuro-Fuzzy Technology, Intelligent Control, Deep Learning-Machine Learning, Fuzzy Logic in Data Analytics, Evolutionary Computing, Fuzzy logic and Artificial Intelligence in Engineering, Social Sciences, Business, Economics, Material Sciences, and others.This book presents the proceedings of the 16th International Conference on Applications of Fuzzy Systems, Soft Computing, and Artificial Intelligence Tools, ICAFS-2022, held in Budva, Montenegro, on August 26-27, 2022. This is a useful guide for academics, practitioners, and graduates in fields of fuzzy logic and soft computing. It allows for increasing of interest in development and applying of these paradigms in various real-life fields.

Smart Energy Management

Author : Kaile Zhou
Publisher : Springer Nature
ISBN 13 : 9811693609
Total Pages : 317 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis Smart Energy Management by : Kaile Zhou

Download or read book Smart Energy Management written by Kaile Zhou and published by Springer Nature. This book was released on 2022-02-04 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a relatively whole view of data-driven decision-making methods for energy service innovation and energy system optimization. Through personalized energy services provision and energy efficiency improvement, the book can contribute to the green transformation of energy system and the sustainable development of the society. The book gives a new way to achieve smart energy management, based on various data mining and machine learning methods, including fuzzy clustering, shape-based clustering, ensemble clustering, deep learning, and reinforcement learning. The applications of these data-driven methods in improving energy efficiency and supporting energy service innovation are presented. Moreover, this book also investigates the role of blockchain in supporting peer-to-peer (P2P) electricity trading innovation, thus supporting smart energy management. The general scope of this book mainly includes load clustering, load forecasting, price-based demand response, incentive-based demand response, and energy blockchain-based electricity trading. The intended readership of the book includes researchers and engineers in related areas, graduate and undergraduate students in university, and some other general interested audience. The important features of the book are: (1) it introduces various data-driven methods for achieving different smart energy management tasks; (2) it investigates the role of data-driven methods in supporting various energy service innovation; and (3) it explores energy blockchain in P2P electricity trading, and thus supporting smart energy management.

Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate

Author : Gui Ye
Publisher : Springer Nature
ISBN 13 : 9811588929
Total Pages : 2252 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate by : Gui Ye

Download or read book Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate written by Gui Ye and published by Springer Nature. This book was released on 2021-06-07 with total page 2252 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers various current and emerging topics in construction management and real estate. Papers selected in this book cover a wide variety of topics such as new-type urbanization, planning and construction of smart city and eco-city, urban–rural infrastructure development, land use and development, housing market and housing policy, new theory and practice of construction project management, big data application, smart construction and BIM, international construction (i.e., belt and road project), green building, off-site prefabrication, rural rejuvenation and eco-civilization and other topics related to construction management and real estate. These papers provide useful references to both scholars and practitioners. This book is the documentation of “The 24th International Symposium on Advancement of Construction Management and Real Estate,” which was held in Chongqing, China.

Data Mining and Machine Learning in Building Energy Analysis

Author : Frédéric Magoules
Publisher : John Wiley & Sons
ISBN 13 : 1118577485
Total Pages : 187 pages
Book Rating : 4.1/5 (185 download)

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Book Synopsis Data Mining and Machine Learning in Building Energy Analysis by : Frédéric Magoules

Download or read book Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-01-05 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Data-driven Modeling and Analysis of Residential Building Energy Consumption and Demand Flexibility

Author : Emily Kawka
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.4/5 (387 download)

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Book Synopsis Data-driven Modeling and Analysis of Residential Building Energy Consumption and Demand Flexibility by : Emily Kawka

Download or read book Data-driven Modeling and Analysis of Residential Building Energy Consumption and Demand Flexibility written by Emily Kawka and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings are responsible for approximately 74% of total electricity consumption, the leading contributor of carbon dioxide emissions in the United States. As initiatives aim toward net zero emissions through electrification and clean energy, building energy efficiency measures are crucial to achieve this clean energy transition. Through measuring energy use, this increases the accuracy of building use assumptions, which drive how energy use reduction is investigated and targeted. As disruptive events and technology shift how occupants use residential buildings, this has the potential to shift how they consume their energy. In this thesis, high resolution, disaggregated energy use data is used to model and analyze energy use for two specific disruptions: the COVID-19 pandemic and electric vehicles (EVs). The first study measures how COVID-19 impacted residential building energy use. The findings of this research indicate an increase in energy use for both weather-dependent loads and weather-independent loads during the COVID-19 pandemic. Additional analyses give insight to the pandemic's impact by household income, demonstrating the lowest and highest income groups experiencing larger increases in consumption while remaining populations experienced smaller shifts. The second study analyzes residential EV charging behavior and models the maximum load reduction potential for demand response in the Midcontinent Independent System Operator (MISO) region. The results of this study indicate relatively consistent charging use patterns across a full year, weekend charging is more distributed throughout the daytime compared to weekday charging, and there are significant opportunities to reduce or shift EV loads during typical peak load periods.

Data Mining and Machine Learning in Building Energy Analysis

Author : Frédéric Magoules
Publisher : John Wiley & Sons
ISBN 13 : 1848214227
Total Pages : 186 pages
Book Rating : 4.8/5 (482 download)

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Book Synopsis Data Mining and Machine Learning in Building Energy Analysis by : Frédéric Magoules

Download or read book Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-02-08 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.

Short-Term Load Forecasting by Artificial Intelligent Technologies

Author : Wei-Chiang Hong
Publisher : MDPI
ISBN 13 : 3038975826
Total Pages : 445 pages
Book Rating : 4.0/5 (389 download)

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Book Synopsis Short-Term Load Forecasting by Artificial Intelligent Technologies by : Wei-Chiang Hong

Download or read book Short-Term Load Forecasting by Artificial Intelligent Technologies written by Wei-Chiang Hong and published by MDPI. This book was released on 2019-01-29 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Short-Term Load Forecasting by Artificial Intelligent Technologies" that was published in Energies

Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications

Author : B Rajanarayan Prusty
Publisher : CRC Press
ISBN 13 : 1040016111
Total Pages : 253 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications by : B Rajanarayan Prusty

Download or read book Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications written by B Rajanarayan Prusty and published by CRC Press. This book was released on 2024-05-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.

Smart Meter Data Analytics

Author : Yi Wang
Publisher : Springer Nature
ISBN 13 : 9811526249
Total Pages : 306 pages
Book Rating : 4.8/5 (115 download)

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Book Synopsis Smart Meter Data Analytics by : Yi Wang

Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.