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.
Highlights
In energy planning, variables such as wind power, solar radiation, CO2 emissions, electricity prices, etc., are predicted [1]
Comparing the speed and accuracy of the new algorithm and available algorithms shows that by Comparing the speed and accuracy of the new algorithm and available algorithms shows that increasing the amount of data under investigation, the accuracy of the new algorithm is maintained by increasing the amount of data under investigation, the accuracy of the new algorithm is while simultaneously the slope of the execution time of the new algorithm is lower than the existing maintained while simultaneously the slope of the execution time of the new algorithm is lower than algorithms. These results show that the big data analysis of the electricity market has good results with the existing algorithms
These results show that the big data analysis of the electricity market has good results with the new algorithm in future
Summary
Variables such as wind power, solar radiation, CO2 emissions, electricity prices, etc., are predicted [1]. Machine-learning methods were used in this paper to search in Big Data of the electricity market with the purpose of developing forecasting techniques. It is assumed that if the electricity market manager can search in a large size of previous data by using intelligent methods, it will be possible to improve the forecasting accuracy of electrical load supply and demand. It was proposed for forecasting the electrical load demand or other variables such as the wind power This technique maintains the improved in accuracy and speed by increasing the length of training data (LOTD). The data analysis speed was considerably important It was pointed out in the Results section (because it will be necessary to predict variables in the shortest possible intervals in smart grids in the electricity market in the foreseeable future).
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