PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION and SAVINGS USING NEURAL NETWORK APPROACH Dissertation Submitted to the School
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PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND SAVINGS USING NEURAL NETWORK APPROACH Dissertation Submitted to The School of Engineering of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree of Doctor of Philosophy in Engineering By Badr Ibrahim Al Tarhuni Dayton, Ohio May, 2019 PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND SAVINGS USING NEURAL NETWORK APPROACH Name: Al Tarhuni, Badr Ibrahim APPROVED BY: Kevin P. Hallinan, Ph.D. Robert J. Brecha, Ph.D. Advisory Committee Chairman Committee Member Professor Professor Mechanical Engineering Department of Physics Andrew Chiasson, Ph.D., P.E. Jun- Ki. Choi, Ph.D. Committee Member Committee Member Assistant Professor Associate Professor Mechanical Engineering Mechanical Engineering Robert J. Wilkens, Ph.D., P.E. Eddy M. Rojas, Ph.D., M.A., P.E. Associate Dean for Research and Innovation Dean, School of Engineering Professor School of Engineering ii ABSTRACT PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND SAVINGS USING NEURAL NETWORK APPROACH Name: Al Tarhuni, Badr Ibrahim University of Dayton Advisor: Dr. Kevin P. Hallinan 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 iii 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 iv 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. v ACKNOWLEDGEMENTS A special thanks to my family. Words can not express how grateful I am to parents, Ibrahim Al Tarhuni and Khadijah Al Tarhuni, for all your support and continuous encouragement throughout my years of study. This accomplishment would not have been possible without them. Furthermore, my greatest thanks go to my wife Rema Ammar who has provided me through moral and emotional support in my life and through the process of researching and writing this dissertation. I am deeply thankful to my three lovely children, Ibrahim, Muath and Hala, who bring me love and joy. I would like to thank my advisor Prof. Kevin Hallinan for the freedom you have given me to find my own path and the guidance and support you offered when needed. The door to Prof. Hallinan office was always open whenever I ran into a trouble spot or had a question about my research or writing. I also want to thank my committee members, Prof. Robert Brecha, Prof. Jun-Ki Choi and Prof. Andrew Chiasson for generously offering their time, support, and guidance. Finally, my sincere appreciation goes to my home country, Libya who funded the whole PhD programme. vi PREFACE As the goal of this work is to predict energy consumption of any residence and then ultimately energy savings from specific energy efficiency measures based upon actual residential building and energy data, it is essential to document that the building set has reasonable variation in characteristics that influence energy consumption. The research described in this dissertation was done in partnership with my colleague Adel Naji. We both were responsible for collecting the data used in the dissertation. The neural network model that was used to predict the energy consumption in chapter 2 was developed by myself. The economic analysis Chapter 3 was done primarily by Adel Naji and myself, except for the section 3.4.4, which assisted by Prof. Jun-Ki Choi. Some of the sections of the dissertation represent shared authorship between myself and Adel Naji. vii TABLE OF CONTENTS ABSTRACT ....................................................................................................................... iii ACKNOWLEDGEMENTS ............................................................................................... vi PREFACE ......................................................................................................................... vii LIST OF FIGURES ............................................................................................................ x LIST OF TABLES ............................................................................................................ xii CHAPTER 1: INTRODUCTION ....................................................................................... 1 CHAPTER 2: PREDICTING RESIDENTIAL HEATING ENERGY CONSUMPTION AND SAVINGS FROM KNOWN ENERGY CHARACTERISTICS AND HISTORICAL ENERGY CONSUMPTION ...................... 5 2.1 Abstract ................................................................................................................ 5 2.2 Background .......................................................................................................... 6 2.3 Methodology ...................................................................................................... 10 2.3.1 Building Data Set .......................................................................................... 10 2.4 Identification of Characteristics Having Greatest Impact on Energy Consumption Using a Random Forest Approach ......................................................... 15 2.5 Artificial Neural Network Model for Predicting Heating Energy...................... 18 2.6 Results and Discussion ....................................................................................... 21 2.6.1 Predicting Energy Consumption ................................................................... 21 2.6.2 Estimating Natural Gas Savings from Retrofit Specific Measures ............... 23 viii 2.7 Validating Savings Using a K-Nearest Neighbor Approach………………...…..29 2.8 Conclusions ........................................................................................................ 31 CHAPTER 3: DATA-BASED APPROACH FOR MOST COST EFFECTIVE RESIDENTIAL ENERGY REDUCTION ....................................................................... 33 3.1 Abstract .............................................................................................................. 33 3.2 Background ........................................................................................................ 34 3.3 Developing a Single ANN Model for All Residences to Predict Heating Consumption and Savings from Individual Energy Efficiency Measure ..................... 36 3.4 Economic Analysis of Sequential Adoption of Most Cost Effective EE Measures ................................................................................................................. 38 3.4.1 Prioritized Energy Savings Among the Aggregate Set of Residences ........