A Novel Electric Load Demand Forecaster Using Taguchi’s Method and Artificial Neural Network
Pp. 180-192 (13)
Albert W.L. Yao, J.H. Sun, H.T. Liao, C.Y. Liu and C.T. Yin
The use of Artificial Neural Network (ANN) for electric load forecasting has been proposed in
many studies. Among these studies, the daily peak load or total load with weather consideration was mostly
predicted in order to dispatch high-quality electricity or assess electric load efficiently for power utilities.
However, load demand forecasting from the standpoint of consumers is seldom discussed. With the global
market competition, enterprises invest in instruments to cut down on large electricity bills of operating costs.
A formal study shows that the regular ANN training model was inadequate to deal with volatile load
patterns, especially in Very Short-Term Electric Demand Forecasting (VSTEDF). In this paper, we present
Taguchi’s and rolling training methods of ANN for VSTEDF. By using this proposed rolling training
model, the electric load demand is predicted precisely every 2 minutes. The forecasting error is smaller than
3%. Compared with the conventional ANN model and Grey model, the proposed Taguchi-ANN-based
predictor has better accuracy in the application of VSTEDF. The improved Taguchi-ANN-based electricity
demand forecaster in conjunction with the PC-based electricity demand-control system is a cost-effective
and efficient means to manage the usage of electricity.
Artificial neural networks (ANN), electric load forecasting.
National Kaohsiung First University of Science and Technology, Taiwan