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

Mesbaholdin Salami, Farzad Movahedi Sobhani and Mohammad Sadegh Ghazizadeh
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Mesbaholdin Salami: Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran 009821, Iran
Farzad Movahedi Sobhani: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 009821, Iran
Mohammad Sadegh Ghazizadeh: Department of Electrical Engineering, Abbaspour School of Engineering, Shahid Beheshti University, Tehran 009821, Iran

Data, 2018, vol. 3, issue 4, 1-26

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.

Keywords: electricity market; electricity supply and demand; Big Data; Monte Carlo method; PSO; Wavelet-NNPSO; smart grid (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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