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Open AccessArticle

Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

1
Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran 009821, Iran
2
Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 009821, Iran
3
Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran 009821, Iran
*
Author to whom correspondence should be addressed.
Received: 4 October 2018 / Revised: 18 October 2018 / Accepted: 21 October 2018 / Published: 23 October 2018
(This article belongs to the Special Issue Data Stream Mining and Processing)
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Abstract

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section. View Full-Text
Keywords: electricity market; electricity supply and demand; Big Data; Monte Carlo method; PSO; Wavelet-NNPSO; smart grid electricity market; electricity supply and demand; Big Data; Monte Carlo method; PSO; Wavelet-NNPSO; smart grid
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MDPI and ACS Style

Salami, M.; Movahedi Sobhani, F.; Ghazizadeh, M.S. Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market. Data 2018, 3, 43.

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