Short-term Electricity Load Forecasting for Thailand

Abstract

Short-term electricity demand or load forecasting is the way of estimating future demand for short time horizon, an hour to one week ahead. Short horizon forecasting helps to maintain secure power system, avoids blackout risk and provides adequate electricity supply. Therefore, accurate forecasting is prime concern for day to day planing and ensuring the stability of electricity system. Many literature show their mean absolute percentage error (MAPE) for short term load forecasting as less than 2%, which is very competitive performance. The methodology behind good performance is that the model should address the driving factors that effect electricity load and employs appropriate estimation techniques. In our work, multiple linear regression model are developed and Bayesian estimation technique is used. Assuming historical load, temperature, daily, and weekly seasonal patterns as the main effecting factors for electricity load consumption, two models Model A, and Model B are constructed. Model A consist historical load and deterministic terms. However, model B consist all variables of Model A plus temperature variables. These models are analyzed based on their forecasting performance for three years out of sample prediction. Our results showed that when temperature variable is included in model, it can improve the overall performance at least by 20%. Performance improvement was higher during morning and evening hours than day hours. Interestingly, evening hours (17 to 22) are statistically not significant.