Performance analysis of short-term electricity demand with meteorological parameters

The quality of short term electricity demand fore-casting is essential for all the energy market players for operation and trading activities. Electricity demand is significantly affected by non linear factors such as climatic condition, calendar and other seasonality have been widely reported in literature. This paper considers parsimonious forecasting models to explain the importance of meteorological parameters for the hourly electricity demand forecasting. Many researchers include only temperature as a major weather factor because it directly influences electricity demand, however other meteorological factors such as relative humidity, wind speed etc. are rarely included in literature. Therefore, the main purpose of this study is to investigate the impact of meteorological variability such as relative humidity, wind speed, solar radiation etc. for short term demand forecasting and 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 the performances for Hokkaido Prefecture, Japan. We applied Bayesian approach to estimate the weight of each parameters with Gibbs sampling and results show overall improvement of mean absolute percentage error (MAPE) performance by 0.015%.