Prediction of energy consumption in existing public buildings using gray-box based models

Energy consumption prediction on buildings can help building owners and operators to reduce energy costs, reduce environmental impact, improve occupant comfort, and optimize building performance. Study aims to develop a prediction model for energy consumption prediction in university campus buildings using machine learning techniques with time series and physics/engineering-based datasets. Time series energy consumption data sets from existing buildings, as well as building physics/engineering data, will be analyzed to estimate campus scale energy consumption. Time series data will be used for heating/cooling and lighting, and physics/engineering data will be used for outdoor data such as outdoor air temperature, relative humidity, and building specific characteristics such as building floor area, floor height, and material type. To improve prediction accuracy, a simulation study will be conducted using a physics-based approach, and a model will be developed. The results of this approach will be used as input for the data-based approach, and a hybrid model will be presented for prediction using deep learning techniques such as LSTM and RNN. Within the scope of the study, studies on energy consumption prediction of existing buildings generally use models containing time series datasets on energy consumption or models containing building physical information. Considering that each of these data impacts energy consumption, evaluating data together helps make more accurate consumption forecasts. However, evaluating these data together is a big problem in itself. Within the scope of the study, predictions will be made for using these two data types together and the advantages and shortcomings of the model results compared to data-based models will be discussed. While previous research has primarily focused on either time series datasets or building physical information, this study will think to be one of the first to evaluate these two data types together in order to provide more accurate energy consumption predictions and generalizable results.