Short Term Load Forecasting on PRECON Dataset

As the electricity market is growing, the need for accurate Short Term Load Forecasting (STLF) is increasing. Electrical grid operators require STLF to plan schedules for power generation plants. With the introduction of intermittent renewable resources, the stakes are now even higher. Developed countries have been fortunate in this regard as most of the research on STLF focused on these countries and developed highly accurate models. There is now a need to focus on developing countries as these are substantial energy markets with thriving economies and high population growth rates. With the 43
^rd
largest economy by GDP and 6
^th
largest nation by population, Pakistan is one such country. As the energy demand of Pakistan is increasing, there is a need to understand the energy demand patterns of its citizens better. PRECON is an electricity consumption dataset of residential buildings in Pakistan that can help in this regard. In this paper, we present preliminary results of applying STLF techniques on PRECON. These initial results show that Multiple Linear Regression and Support Vector Regression perform better than Artificial Neural Network ambient temperature and autoregressive attributes as input variables. The results also discuss various performance metrics, such as ME, RMSE, and MPE. The results show a unique phenomenon, load shedding, not experienced in developed countries.