Minutely Active Power Forecasting Models Using Neural Networks

Abstract

Power forecasting is an integral part of the Demand Response design philosophy for power systems, enabling utility companies to understand the electricity consumption patterns of their customers and adjust price signals accordingly, in order to handle load demand more effectively. Since there is an increasing interest in real-time automation and more flexible Demand Response programs that monitor changes in the residential load profiles and reflect them according to changes in energy pricing schemes, high granularity time series forecasting is at the forefront of energy and artificial intelligence research, aimed at developing machine learning models that can produce accurate time series predictions. In this study we compared the baseline performance and structure of different types of neural networks on residential energy data by formulating a suitable supervised learning problem, based on real world data. After training and testing long short-term memory (LSTM) network variants, a convolutional neural network (CNN), and a multi-layer perceptron (MLP), we observed that the latter performed better on the given problem, yielding the lowest mean absolute error and achieving the fastest training time.