Jihoon Moon, Sanghoon Jun, Jinwoong Park, Young-Hwan Choi, Eenjun Hwang

KIPS Transactions on Computer and Communication Systems, Vol. 5, No. 10, pp. 293-302, Oct. 2016
10.3745/KTCCS.2016.5.10.293,   PDF Download:
Keywords: Electric Load Forecasting, Educational Institution, Support Vector Regression, Artificial neural network
Abstract

Since the electricity is produced and consumed simultaneously, predicting the electric load and securing affordable electric power are necessary for reliable electric power supply. In particular, a university campus is one of the highest power consuming institutions and tends to have a wide variation of electric load depending on time and environment. For these reasons, an accurate electric load forecasting method that can predict power consumption in real-time is required for efficient power supply and management. Even though various influencing factors of power consumption have been discovered for the educational institutions by analyzing power consumption patterns and usage cases, further studies are required for the quantitative prediction of electric load. In this paper, we build an electric load forecasting model by implementing and evaluating various machine learning algorithms. To do that, we consider three building clusters in a campus and collect their power consumption every 15 minutes for more than one year. In the preprocessing, features are represented by considering periodic characteristic of the data and principal component analysis is performed for the features. In order to train the electric load forecasting model, we employ both artificial neural network and support vector machine. We evaluate the prediction performance of each forecasting model by 5-fold cross-validation and compare the prediction result to real electric load.

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