Distribution Level Building Load Prediction Using Deep Learning

Load prediction in distribution grids is an important means to improve energy supply scheduling, reduce the production cost, and support emission reduction. Determining accurate load predictions has become more crucial than ever as electrical load patterns are becoming increasingly complicated due to the versatility of the load profiles, the heterogeneity of individual load consumptions, and the variability of consumer-owned energy resources. However, despite the increase of smart grids technologies and energy conservation research, many challenges remain for accurate load prediction using existing methods. This dissertation investigates how to improve the accuracy of load predictions at the distribution level using artificial intelligence (AI), and in particular deep learning (DL), which have already shown significant progress in various other disciplines. Existing research that applies the DL for load predictions has shown improved performance compared to traditional models. The current research using conventional DL tends to be modeled based on the developer\u27s knowledge. However, there is little evidence that researchers have yet addressed the issue of optimizing the DL parameters using evolutionary computations to find more accurate predictions. Additionally, there are still questions about hybridizing different DL methods, conducting parallel computation techniques, and investigating them on complex smart buildings. In addition, there are still questions about disaggregating the net metered load data into load and behind-the-meter generation associated with solar and electric vehicles (EV). The focus of this dissertation is to improve the distribution level load predictions using DL. Five approaches are investigated in this dissertation to find more accurate load predictions. The first approach investigates the prediction performance of different DL methods applied for energy consumption in buildings using univariate time series datasets, where their numerical results show the effectiveness of recursive artificial neural networks (RNN). The second approach studies optimizing time window lags and network\u27s hidden neurons of an RNN method, which is the Long Short-Term Memory, using the Genetic Algorithms, to find more accurate energy consumption forecasting in buildings using univariate time series datasets. The third approach considers multivariate time series and operational parameters of practical data to train a hybrid DL model. The fourth approach investigates parallel computing and big data analysis of different practical buildings at the DU campus to improve energy forecasting accuracies. Lastly, a hybrid DL model is used to disaggregate residential building load and behind-the-meter energy loads, including solar and EV