Hybrid Ensemble Deep Learning-Based Approach for Time Series Energy Prediction

Pyae Pyae Phyo,Yung-Cheol Byun

doi:10.3390/sym13101942

Abstract

The energy manufacturers are required to produce an accurate amount of energy by meeting the energy requirements at the end-user side. Consequently, energy prediction becomes an essential role in the electric industrial zone. In this paper, we propose the hybrid ensemble deep learning model, which combines multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN-LSTM to improve the forecasting performance. These DL architectures are more popular and better than other machine learning (ML) models for time series electrical load prediction. Therefore, hourly-based energy data are collected from Jeju Island, South Korea, and applied for forecasting. We considered external features associated with meteorological conditions affecting energy. Two-year training and one-year testing data are preprocessed and arranged to reform the times series, which are then trained in each DL model. The forecasting results of the proposed ensemble model are evaluated by using mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Error metrics are compared with DL stand-alone models such as MLP, CNN, LSTM, and CNN-LSTM. Our ensemble model provides better performance than other forecasting models, providing minimum MAPE at 0.75%, and was proven to be inherently symmetric for forecasting time-series energy and demand data, which is of utmost concern to the power system sector.

Highlights

The energy sector is one of the essential factors in modern society, and the required amount of energy between supply and demand should be balanced
The generated results between the proposed ensemble model and other deep learning (DL) standalone models are compared monthly error metrics on test predictions from June 2018 to Monthly mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are evaluated to make a comparison among all models, as revealed in Tables 3–5, respectively
All models provide reasonable forecast values, with errors varying from 3% to 5% of MAPEs in all months, except February

Summary

Introduction

The energy sector is one of the essential factors in modern society, and the required amount of energy between supply and demand should be balanced. Energy forecasting plays a vital role in helping energy manufacturers. It is helpful in the improvement of energy management systems, planning, and operation [1,2]. Short-term hourly energy forecasting is conducted because it is an effectively helpful tool for reducing energy generating and operating costs, ensure power system security, and perform short-term scheduling functions. AI-based machine learning (ML) models have been widely used in medicine, business, communications, and industrial process control as nonlinear time series problems can be solved. The training process of ML models could cost a longer computational time during the backpropagation process if there were multiple layers in the network. There is no interconnection between each layer in the traditional ML that causes the lack of information for time series data

Methods

Results

Conclusion