Abstract—In this paper, two models were proposed for week-ahead forecasting of temperature driven electricity load, which are a time series model and an Artificial Neural Network (ANN) model. Over the week-long (“future”) forecasting horizon, predicted temperature from ANN was used as it is shown that ANN produced more accurate temperature prediction. For the time series model, Seasonal Autoregressive Integrated Moving Average with eXogenous variables (SARIMAX) scheme was proposed. A method called “pre-whitening” was used to determine the lagged effect of temperature on electricity load. Comparison between ANN model and SARIMAX model was conducted to see which one gave a better forecasting performance. The forecast performance was characterized by two statistical estimates, the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE). The results showed that while the ANN model behaved better in the estimation stage, its performance got worse than SARIMAX model in the forecasting stage.
Index Terms—Artificial neural networks (ANN), load forecasting, SARIMAX, short-term, temperature forecasting, time series.
Nengbao Liu and Afshin Afshari are with the Department of Engineering System and Management Program, Masdar Institute of Science and Technology, Abu Dhabi, UAE. (e-mail: nliu@masdar.ac.ae, aafshin@masdar.ac.ae).
Vahan Babushkin is with the Department of Computing and Inforamtion Science, Masdar Institute of Science and Technology, Abu Dhabi, UAE. (e-mail: vbabushkin@masdar.ac.ae).
[PDF]
Cite:Nengbao Liu, Vahan Babushkin, and Afshin Afshari, "Short-Term Forecasting of Temperature Driven Electricity Load Using Time Series and Neural Network Model," Journal of Clean Energy Technologies vol. 2, no. 4, pp. 327-331, 2014.
