Next Article in Journal
Analysis and Optimization of Material Flow inside the System of Rotary Coolers and Intake Pipeline via Discrete Element Method Modelling
Previous Article in Journal
Assessing the Demand Side Management Potential and the Energy Flexibility of Heat Pumps in Buildings
Previous Article in Special Issue
Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Energies 2018, 11(7), 1848; https://doi.org/10.3390/en11071848

Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables

Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan (R.O.C)
*
Author to whom correspondence should be addressed.
Received: 23 June 2018 / Revised: 10 July 2018 / Accepted: 11 July 2018 / Published: 14 July 2018
(This article belongs to the Special Issue Forecasting Models of Electricity Prices 2018)
View Full-Text   |   Download PDF [3837 KB, uploaded 14 July 2018]   |  

Abstract

Electricity is important because it is the most common energy source that we consume and depend on in our everyday lives. Consequently, the forecasting of electricity sales is essential. Typical forecasting approaches often generate electricity sales forecasts based on certain explanatory variables. However, these forecasting approaches are limited by the fact that future explanatory variables are unknown. To improve forecasting accuracy, recent hybrid forecasting approaches have developed different feature selection techniques (FSTs) to obtain fewer but more significant explanatory variables. However, these significant explanatory variables will still not be available in the future, despite being screened by effective FSTs. This study proposes the autoregressive integrated moving average (ARIMA) technique to serve as the FST for hybrid forecasting models. Aside from the ARIMA element, the proposed hybrid models also include artificial neural networks (ANN) and multivariate adaptive regression splines (MARS) because of their efficient and fast algorithms and effective forecasting performance. ARIMA can identify significant self-predictor variables that will be available in the future. The significant self-predictor variables obtained can then serve as the inputs for ANN and MARS models. These hybrid approaches have been seldom investigated on the electricity sales forecasting. This study proposes several forecasting models that do not require explanatory variables to forecast the industrial electricity, residential electricity, and commercial electricity sales in Taiwan. The experimental results reveal that the significant self-predictor variables obtained from ARIMA can improve the forecasting accuracy of ANN and MARS models. View Full-Text
Keywords: forecast; electricity sales; autoregressive integrated moving average (ARIMA); artificial neural networks; multivariate adaptive regression splines; hybrid forecast; electricity sales; autoregressive integrated moving average (ARIMA); artificial neural networks; multivariate adaptive regression splines; hybrid
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Shao, Y.E.; Tsai, Y.-S. Electricity Sales Forecasting Using Hybrid Autoregressive Integrated Moving Average and Soft Computing Approaches in the Absence of Explanatory Variables. Energies 2018, 11, 1848.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top