Evaluation of Artificial Intelligence Algorithms for Predicting Power Consumption in University Campus Microgrid

Abstract

Prediction of power consumption in smart grid and microgrid systems has become a major issue, it represents one of the most important factors in energy management systems (EMS). Recently, several models based on artificial intelligence techniques have been proposed to predict electricity consumption and production, mainly for household energy efficiency. In this paper, we evaluate different algorithms to predict the daily power consumption in a university campus microgrid context. We investigate the implementation of different prediction models in three different real datasets, considering four performance indicators to analyze their accuracy, such as Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, and R-square. Different approaches using time series: ARIMA, SARIMA, machine learning: SVM, XGBOOST, and deep learning: RNN, LSTM, and LSTM-RNN hybrid model were evaluated. Results prove that deep learning approaches achieve better results than time series and machine learning forecasting models. In this work, we prove that the RNN-LSTM hybrid model is the most appropriate model for university campus microgrid case with an accuracy between 83% and 93%.