Next Article in Journal
The Effects of Topology on Throughput Capacity of Large Scale Wireless Networks
Previous Article in Journal
Computer-Aided Identification and Validation of Intervenability Requirements
Article Menu

Export Article

Open AccessArticle
Information 2017, 8(1), 31; doi:10.3390/info8010031

Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms

1
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
2
Faculty of Information Technology, University of Transport Technology, Hanoi 100000, Vietnam
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Received: 26 December 2016 / Revised: 6 March 2017 / Accepted: 7 March 2017 / Published: 10 March 2017
View Full-Text   |   Download PDF [2906 KB, uploaded 20 March 2017]   |  

Abstract

Electricity demand forecasting plays an important role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate prediction of electricity demands is therefore vital. In this study, artificial neural networks (ANNs) trained by different heuristic algorithms, including Gravitational Search Algorithm (GSA) and Cuckoo Optimization Algorithm (COA), are utilized to estimate monthly electricity demands. The empirical data used in this study are the historical data affecting electricity demand, including rainy time, temperature, humidity, wind speed, etc. The proposed models are applied to Hanoi, Vietnam. Based on the performance indices calculated, the constructed models show high forecasting performances. The obtained results also compare with those of several well-known methods. Our study indicates that the ANN-COA model outperforms the others and provides more accurate forecasting than traditional methods. View Full-Text
Keywords: Cuckoo Optimization Algorithm; Gravitational Search Algorithm; neural networks; forecasting; monthly electricity demand Cuckoo Optimization Algorithm; Gravitational Search Algorithm; neural networks; forecasting; monthly electricity demand
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Chen, J.-F.; Lo, S.-K.; Do, Q.H. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information 2017, 8, 31.

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]
Information EISSN 2078-2489 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top