Performance Analysis of LSTMs and Fbprophet Models for Short Term Load Forecasting

Abstract

With the advent of smart grids, accurate electric load forecasting has become more essential since it may assist power companies in improving load scheduling and reducing surplus energy output. Short term load forecasting (STLF) is gaining popularity owing to its utility in energy usage, demand-side management, energy storage, peak load forecasting and minimize electricity production costs. This study offers four artificial intelligence-based models to enhance 168-hours prediction accuracy. These models are long short term memory (LSTM), bidirectional LSTM (Bi-LSTM), Conv2D LSTM and Fbprophet. The models are trained with hourly energy consumption data of four years. After training and testing, it is depicted that bidirectional LSTM can predict more precisely than other models with an MAPE of 3.59. The MAPE of Conv2D LSTM, LSTM and Fbprophet are found 3.95, 4.91 and 7.75 accordingly. Since bidirectional LSTM utilizes the LSTM regular model twice, they usually have more accuracy than conventional LSTM. The use of bidirectional LSTM may thus make the demand response system more efficient.