Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity concept

This paper proposes a new hybrid season specific approach to incorporate the seasonality effect in short term load forecasting (STLF). A new season specific similarity concept (SSSC) is utilized to perceive the season specific meteorological necessities (seasonality effect) and integrates them in STLF process. The proposed approach is based on firefly algorithm (FA), support vector machine (SVM) and the new SSSC. The study is conducted in Assam, India and the proposed approach is designed to forecast load during different seasonal native meteorological conditions. Four case studies in four different seasons of a calendar year are carried out. The consideration of seasonality effect is found essential for a precise STLF under diverse seasonal meteorological conditions. This is because the electric load is influenced by different meteorological variables depending on different seasons. The numerical application of the proposed approach demonstrates higher forecasting accuracy in comparison to traditional approach of integrating temperature into STLF without considering any seasonality effect. To uphold the efficacy of the proposed approach, forecasting results are also compared with another traditional approach of integrating multiple meteorological variables into STLF without any seasonal considerations. Hence, the robustness of proposed approach is approved by its superior forecasting ability in all cases. •Offers a season specific approach for short term load forecasting in the study area.•Evaluation of season specific effective meteorological variables on load demand.•Proposes new season specific similarity concept to integrate the seasonality effect.•Integration of ineffective meteorological variable misguide the forecasting process.•The results affirm the superiority of the proposed approach in load forecasting.