Predicting Residential Heating Energy Consumption And Savings Using Neural Network Approach

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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.

Data Mining and Machine Learning in Building Energy Analysis

Author : Frédéric Magoules
Publisher : John Wiley & Sons
ISBN 13 : 1118577590
Total Pages : 186 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 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.

Prediction of Energgy Consumption of Residential Buildings by Artificial Neural Networks and Fuzzy Logic

Author : Cihan Turan
Publisher :
ISBN 13 :
Total Pages : 162 pages
Book Rating : 4.:/5 (855 download)

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Book Synopsis Prediction of Energgy Consumption of Residential Buildings by Artificial Neural Networks and Fuzzy Logic by : Cihan Turan

Download or read book Prediction of Energgy Consumption of Residential Buildings by Artificial Neural Networks and Fuzzy Logic written by Cihan Turan and published by . This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are several ways to attempt to forecast building energy consumption. Different techniques, varying from simple regression to dynamic models that are based on physical principles, can be used for simulation. A frequent hypothesis for all these models is that the input variables should be based on realistic data when they are available, otherwise the evaluation of energy consumption might be under or over estimated. The aim of this thesis is to create simple models based on artificial intelligence methods (artificial neural networks and fuzzy logic) as predicting tools and to compare these methods with a building energy performance software (KEP-IYTE ESS). Architectural projects and heat load calculation reports of 148 apartment buildings (5-13 storey) from three municipalities in Ġzmir provide the input data for the models and software. Building energy consumption is modeled as a function of zoning status, heating system type, number of floors, wall overall heat transfer coefficient, glass type, area/volume ratio, existence of insulation, total external surface area, orientation, number of flats, total external surface area/total useful area, total windows area/total external surface area, width/length, total wall area/total useful floor area, total lighting requirement/total useful floor area and total wall area. Four different artificial neural network models and one fuzzy logic model were constructed, trained, tested and the results were compared with the software outcomes. The lowest mean absolute percentage error (MAPE) and mean absolute deviation (MAD) of ANN models appeared to be 4.1% and 6.57, respectively, which shows that ANN can make accurate predictions. On the other hand, fuzzy model gave an 4.86% and 7.59 of MAPE and MAD, respectively, which can be considered as sufficient accuracy.

A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households

Download A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households PDF Online Free

Author : Rochus Niemierko
Publisher : GRIN Verlag
ISBN 13 : 3346020517
Total Pages : 74 pages
Book Rating : 4.3/5 (46 download)

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Book Synopsis A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households by : Rochus Niemierko

Download or read book A D-Vine Copula-Based Quantile Regression Approach for the Prediction of Heating Energy Consumption. Using Historical Data for German Households written by Rochus Niemierko and published by GRIN Verlag. This book was released on 2019-09-23 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2018 in the subject Economics - Statistics and Methods, grade: 1,0, University of Augsburg, language: English, abstract: The aim of this thesis is to add to the as of yet mostly missing literature on how a D-vine copula based quantile regression model can be used to predicte the accurate level of energy consumption. Energetic retrofitting of residential buildings is poised to play an important role in the achievement of ambitious global climate targets. A prerequisite for purposeful policy-making and private investments is the accurate prediction of energy consumption. Building energy models are mostly based on engineering methods quantifying theoretical energy consumption. However, a performance gap between predicted and actual consumption has been identified in literature. Data- driven methods using historical data can potentially overcome this issue. The D-vine copula-based quantile regression model used in this study achieved very good fitting results based on a representative data set comprising 25,000 German households. The findings suggest that quantile regression increases transparency by analyzing the entire distribution of heating energy consumption for individual building characteristics. More specifically, the analyses reveal the following exemplary insights. First, for different levels of energy efficiency, the rebound effect exhibits cyclical behavior and significantly varies across quantiles. Second, very energy-conscious and energy-wasteful households are prone to more extreme rebound effects. Third, with regards to the performance gap, heating energy demand of inefficient buildings is systematically underestimated, while it is overestimated for efficient buildings. Therefore, The remainder of this thesis is organized as follows. Section 2 presents a concise categorization of building energy models. Section 3 presents existing data-driven methods used for the pre-diction of heating energy consumption in the residential sector. Next, Section 4 elaborates on vine copula-based quantile regression. This is followed by a description of the data employed in Section 5. Section 6 presents the empirical results and Section 7 provides the practical im-plications and contribution of the quantile regression approach introduced. Finally, the conclu-sions and limitations of this thesis are discussed in Section 8.

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.

Automated Machine Learning

Author : Frank Hutter
Publisher : Springer
ISBN 13 : 3030053180
Total Pages : 223 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Automated Machine Learning by : Frank Hutter

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Nearly Zero Energy Building Refurbishment

Author : Fernando Pacheco Torgal
Publisher : Springer Science & Business Media
ISBN 13 : 1447155238
Total Pages : 655 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Nearly Zero Energy Building Refurbishment by : Fernando Pacheco Torgal

Download or read book Nearly Zero Energy Building Refurbishment written by Fernando Pacheco Torgal and published by Springer Science & Business Media. This book was released on 2013-10-22 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recast of the Energy Performance of Buildings Directive (EPBD) was adopted by the European Parliament and the Council of the European Union on 19 May 2010. For new buildings, the recast fixes 2020 as the deadline for all new buildings to be “nearly zero energy” (and even sooner for public buildings – by the end of 2018). This book gives practitioner an important tool to tackle the challenges of building refurbishment towards nearly zero energy. This book is welcome at this time and sets the scene for professionals whether practitioners or researchers to learn more about how we can make whether old or new buildings more efficient and effective in terms of energy performance.

Neural Smithing

Author : Russell Reed
Publisher : MIT Press
ISBN 13 : 0262181908
Total Pages : 359 pages
Book Rating : 4.2/5 (621 download)

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Book Synopsis Neural Smithing by : Russell Reed

Download or read book Neural Smithing written by Russell Reed and published by MIT Press. This book was released on 1999-02-17 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial neural networks are nonlinear mapping systems whose structure is loosely based on principles observed in the nervous systems of humans and animals. The basic idea is that massive systems of simple units linked together in appropriate ways can generate many complex and interesting behaviors. This book focuses on the subset of feedforward artificial neural networks called multilayer perceptrons (MLP). These are the mostly widely used neural networks, with applications as diverse as finance (forecasting), manufacturing (process control), and science (speech and image recognition). This book presents an extensive and practical overview of almost every aspect of MLP methodology, progressing from an initial discussion of what MLPs are and how they might be used to an in-depth examination of technical factors affecting performance. The book can be used as a tool kit by readers interested in applying networks to specific problems, yet it also presents theory and references outlining the last ten years of MLP research.

Graph-Based Model for Building Energy Prediction

Author : Atefeh Shamloo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (144 download)

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Book Synopsis Graph-Based Model for Building Energy Prediction by : Atefeh Shamloo

Download or read book Graph-Based Model for Building Energy Prediction written by Atefeh Shamloo and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurate and timely forecasting of building energy use is critical in mitigating the impacts of climate change. Buildings consume a significant amount of energy worldwide, and the ability to accurately predict their energy usage can inform energy-saving strategies, reduce energy waste, and promote sustainable energy practices. Therefore, timely and precise building energy prediction is essential for achieving global energy conservation goals and mitigating the effects of climate change. Many factors contribute to the energy efficiency of buildings such as space layout design, weather and location of the building. The arrangement of space plays a crucial role in the development of building design and can exert a considerable impact on the energy efficiency of the environment. However, the existing data-driven methods do not effectively take the space relations into account in order to predict the energy consumption of buildings. On the other hand, physic-based models can consider spatial relations by including multiple zones, but it can be computationally intensive and time-consuming. This study aims to investigate the impact of space relationships in a layout on building energy usage and aims to develop a model that can effectively integrate space information and their relations into building prediction methods without significantly increasing computational costs. Thus, the study seeks to address the limitations of existing data-driven building energy prediction models, which often neglect the complex spatial relationships in the building layout or rely on computationally intensive physics-based models. Specifically, this study proposes to use graph neural network for building energy prediction by utilizing the graph structure to abstract building space relations and integrate the space relations in model prediction. The Spatial-temporal Graph Neural Networks, which are an extension of Graph Neural Networks, have been developed to consider the temporal factors for hourly building energy use prediction. This study selects an office building layout as a baseline model to simulate the energy consumption of the building. To investigate the impact of layout variations on energy consumption, we systematically altered the arrangement of spaces within an office building. In this layout we have different functions with different setpoint temperatures. The simulations revealed that altering the spatial arrangement had a significant impact on energy consumption. The maximum annual heating difference observed among different layouts reached around 12%, highlighting the potential for substantial energy savings through strategic spatial planning. Similarly, the cooling energy consumption varied about 8% across the various layouts. Building upon these insights, this research develops a spatial temporal graph neural network model to construct a time-series machine learning model for the building, enabling to forecast the energy consumption. In order to assess the effectiveness of the proposed Spatial-Temporal Graph Neural Network model, a model comparison was conducted, considering alternative data-driven and regression-based approaches commonly employed in building energy prediction. Specifically, the ST-GNN was compared against Extreme Gradient Boosting (XGBoost) and Linear Regression (LR) models, which are widely used for their simplicity and interpretability. These models were chosen to represent the benchmark against which the ST-GNN's performance in capturing spatial and temporal dependencies within the building layout. Since the focus of this study was to develop and implement a new method to assess the impact of spatial relations on energy prediction using ST-GCN model, the other methods (e.g., XGBoost and LR) used in this study serve as baseline models without optimization or enhancements, so the impact of boundary conditions in XGBoost and LR models have not been considered. ST-GCN RMSE and MAPE results showed that the model did a good job predicting the hourly energy consumption, showcasing the importance of including space relationships in predicting the energy consumption. Root Mean Square Error (RMSE) for ST-GNN compared to XGBoost and LR across different seasons improved. The improvement of the ST-GNN occurred across all seasons, with a 33.85% reduction in RMSE during Summer, a 31.60% reduction in Fall, 36.77% reduction in Winter, and a 17.97% reduction in spring. These results emphasize the importance of considering spatial relationships in predicting energy consumption, and the research findings indicate that the ST-GCN model is robust and suitable for similar energy prediction applications. However, further studies are needed to assess the model's performance across a wider range of cases.

2024 the 8th International Conference on Energy and Environmental Science (ICEES 2024)

Author : Yanan Liu
Publisher : Springer Nature
ISBN 13 : 3031639014
Total Pages : 1052 pages
Book Rating : 4.0/5 (316 download)

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Book Synopsis 2024 the 8th International Conference on Energy and Environmental Science (ICEES 2024) by : Yanan Liu

Download or read book 2024 the 8th International Conference on Energy and Environmental Science (ICEES 2024) written by Yanan Liu and published by Springer Nature. This book was released on with total page 1052 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Estimating Construction Costs

Author : Robert Leroy Peurifoy
Publisher : McGraw-Hill
ISBN 13 : 9780071239455
Total Pages : 560 pages
Book Rating : 4.2/5 (394 download)

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Book Synopsis Estimating Construction Costs by : Robert Leroy Peurifoy

Download or read book Estimating Construction Costs written by Robert Leroy Peurifoy and published by McGraw-Hill. This book was released on 2001-12-01 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robert Peurifoy was a giant in the field of construction engineering and authored several books during his lifetime. This book last published in 1989 and will capitalize on the well-known name of the author. In this edition, computer calculations of costs and of modeling have been added as well as updated statistics, computer related examples and new problems. Civil, Environmental, and Construction Management Engineering Majors and Professionals will benefit from having this title on their shelf.This edition retains the conceptual strengths of the Peurifoy approach and organization from the previous edition but the new problems and computer-based examples and new up-to-date construction data make it the only choice in academia or industry.

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.

Python-based Deep-Learning Methods for Energy Consumption Forecasting

Author : Josep Roman Cardell
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (122 download)

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Book Synopsis Python-based Deep-Learning Methods for Energy Consumption Forecasting by : Josep Roman Cardell

Download or read book Python-based Deep-Learning Methods for Energy Consumption Forecasting written by Josep Roman Cardell and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In a society where we do nothing but increase the use of electricity in our daily life, en-ergy consumption and the corresponding management is a major issue. The predictionof electric energy demand is a key component, for the power system operators, in themanagement of the electrical grid. The importance of forecasting a particular house-hold daily energy consumption does concern the end-user too, by reason of the designand sizing of a suitable renewable energy system and energy storage.The aim of this thesis is to develop and train a computing system capable of predict-ing, with best accuracy as possible, electricity consumption at household-level. Thispaper presents a Short Term Load Forecasting (STLF) with Artificial Neural Networks(ANN), which lead to accurate results in spite of the dwelling consumption unpre-dictability. The recorded data, containing the daily track of electricity consumption overa particular household from 2015 to 2018, was analysed. Subsequently, a study over theANN architecture and training algorithms was carried out in order to define a robustmodel. Furthermore, several experiments were conducted with different models, con-taining distinct inputs, aiming to compare the relevance of a diversity of parametersfor the network's training. Finally, the forecasting of the optimal models, created withthe insights collected over the whole research, was performed and compared in severalspecially selected time periods.The results showed how with the appropriate inputs and selection of hyperparame-ters, a shallow ANN can provide certain accuracy on the forecasting of electric energydemand. As well as a methodology to develop and train an artificial neural network.

Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019)

Author : Zhaojun Wang
Publisher : Springer Nature
ISBN 13 : 9811395284
Total Pages : 1492 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019) by : Zhaojun Wang

Download or read book Proceedings of the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019) written by Zhaojun Wang and published by Springer Nature. This book was released on 2020-03-19 with total page 1492 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 11th International Symposium on Heating, Ventilation and Air Conditioning (ISHVAC 2019), with a focus on HVAC techniques for improving indoor environment quality and the energy efficiency of heating and cooling systems. Presenting inspiration for implementing more efficient and safer HVAC systems, the book is a valuable resource for academic researchers, engineers in industry, and government regulators.

Advances in Neural Networks – ISNN 2019

Author : Huchuan Lu
Publisher : Springer
ISBN 13 : 3030228088
Total Pages : 630 pages
Book Rating : 4.0/5 (32 download)

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Book Synopsis Advances in Neural Networks – ISNN 2019 by : Huchuan Lu

Download or read book Advances in Neural Networks – ISNN 2019 written by Huchuan Lu and published by Springer. This book was released on 2019-06-26 with total page 630 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 11554 and 11555 constitutes the refereed proceedings of the 16th International Symposium on Neural Networks, ISNN 2019, held in Moscow, Russia, in July 2019. The 111 papers presented in the two volumes were carefully reviewed and selected from numerous submissions. The papers were organized in topical sections named: Learning System, Graph Model, and Adversarial Learning; Time Series Analysis, Dynamic Prediction, and Uncertain Estimation; Model Optimization, Bayesian Learning, and Clustering; Game Theory, Stability Analysis, and Control Method; Signal Processing, Industrial Application, and Data Generation; Image Recognition, Scene Understanding, and Video Analysis; Bio-signal, Biomedical Engineering, and Hardware.

Integrated Spatial and Energy Planning

Author : Gernot Stoeglehner
Publisher : Springer
ISBN 13 : 3319318705
Total Pages : 126 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Integrated Spatial and Energy Planning by : Gernot Stoeglehner

Download or read book Integrated Spatial and Energy Planning written by Gernot Stoeglehner and published by Springer. This book was released on 2016-04-02 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on spatial planning – an important determinant of energy saving and renewable energy supply. Revealing the key driving forces for spatial development supporting the shift towards energy efficiency and renewable energy supplies, it shows the importance of integrated spatial and energy planning approaches for a timely and sustainable change of energy systems, thus supporting policies of climate protection. As operating within the context of renewable energy sources is becoming a major policy issue at the international, European and national level, spatial dimensions of renewable energy systems as well as challenges, barriers and opportunities in different spatial contexts become more important. This book analyses not only the fundamental system interrelations between resources, technologies and consumption patterns with respect to energy, but also the links to the spatial context, and provides guidelines for researchers as well as practitioners in this new, emerging field. It presents innovative analytical tools to solve real-world problems and discusses the most important fields of action in integrated spatial and energy planning including planning contents, planning visions and principles as well as planning process design and planning methodology.

Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings

Download Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings PDF Online Free

Author : Yafeng Lei
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (69 download)

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Book Synopsis Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings by : Yafeng Lei

Download or read book Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings written by Yafeng Lei and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.