Abstract
Short-term load forecasting (STLF) plays an important role in power system planning and operation and is also fundamental in many applications such as electricity market operations and management of complex asset portfolios. Considering the importance of load forecasting in electric supply industry, the NPower Forecasting Challenge 2015 was organized by RWE NPower, in which students and professionals were invited to predict daily usage of a group of customers based on weather and other relevant data, such as the type of day. Around 80 teams participated in the competition. In this paper, a methodology to solve this problem using artificial neural networks (ANN) has been explained, which the author applied for taking part in this competition. The applied forecasting model is an ensemble of feed-forward neural network (FFNN) and multiple linear regression (MLR) technique. It has been observed that forecasting accuracy gets improved by proper combination and the performance of the model is reasonable.
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