Fuzzy ARTMAP and GARCH‐based hybrid model aided with wavelet transform for short‐term electricity load forecasting

Abstract

With the evolution of the electricity market into a restructured smart version, load forecasting has emerged as an eminent research domain. Many forecasting models have been proposed by researchers for electricity price and load forecasting. This state of art introduces a load time series modeled with a hybrid technique culminating from the logical amalgamation of GARCH, a conventional hard computing method, Fuzzy ARTMAP, an artificial intelligence-based soft computing technique, and wavelet transform, for treating the load time series. The study investigates into the ability of the proposed hybrid model in tackling the electricity load time series forecasting problems. The work under this study also includes comparisons drawn among models which use either one or two of the mentioned techniques and the model proposed. Results certify the efficacy and effectiveness of the model over others. 15

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

“…Kavousi-Fard and Kavousi-Fard [8] proposed a new hybrid model based on ARIMA for short-term load forecasting. Takiyar et al [9] developed a GARCH-based hybrid model for short-term electric load forecasting. Garcia-Ascanio and Mate [10] utilized VAR-based interval time series model to forecast electric load forecasting.…”