The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting demand for charging stations one critical issues while mitigating challenges caused by increased penetration EVs. Uncovering load-affecting features station can be beneficial improving forecasting accuracy. Existing studies mostly forecast electricity based on load profiling. It difficult pub...

The dayahead short term load forecasting (STLF) is a necessary daily task for power dispatch. Short term load forecasting is essential for unit commitment, economic allocation of generation, maintenance schedules. This paper presents a solution methodology using fuzzy logic for short term load forecasting. Fuzzy logic approach is implemented on weather sensitive data and historical load data fo...

Abstract To effectively mine historical data information and improve the accuracy of short-term load prediction, this paper aims at characteristics time series nonlinear power load. Deep learning for forecasting has received a lot attention in recent years, it become popular analysis electricity forecasting. Long memory (LSTM) gated recurrent unit (GRU) are specifically designed time-series dat...