Forecasting power consumption for higher educational institutions based on machine learning

Abstract

Electric power consumption is affected by diverse factors. In particular, a university campus, which is one of the highest power consuming institutions, shows a wide variation of electric load depending on time and environment. For stable operation of such institution, reliable electric power supply should be guaranteed. One of possible methods to do that is to forecast the electric load accurately and supply power accordingly. Even though various influencing factors of power consumption have been discovered for educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative forecasting of their electric load. In this paper, we build a power consumption forecasting model using various machine learning algorithms. To evaluate their effectiveness, we consider four building clusters in a university and collect their power consumption data of 15-min interval over more than one year. For the data, we first extract features based on the periodic characteristic and then perform the principal component analysis and factor analysis for the features. We build two electric load forecasting models using artificial neural network and support vector regression. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to actual electric load. The experimental results show that the two forecasting models can achieve average error rate of 3.46–10 % for all clusters.