(untitled) (Kathford Journal of Engineering and Management, 2018)
The accuracy of short term electricity demand forecasting is essential for operation and trading activities on energy market. This paper considers a parsimonious forecasting model to explain the importance of sophisticated weather parameters for hourly electricity demand forecasting. Temperature is the major factor that directly influence electricity demand, but what about the affect of other weather factors such as relative humidity, wind speed etc. on short term electricity demand forecasting, is the prime research question and this paper analyzed it quantitatively. We demonstrate three different multiple linear models including auto-regressive moving average ARMA (2,6) models with and without some exogenous weather variables to compare with performance for Hokkaido Prefecture, Japan. Since, Bayesian approach is used to estimate the weight of each variables with Gibbs sampling, it generates the weight of coefficients in terms of distribution as our interest. The performance of each models for complete one year out sample prediction shows that the average improvement of hourly forecast by 1 to 2 % can be achieve by including such weather factors.