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Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices
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AI
DOI：10.1016/j.jup.2022.101444.png)
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Abstract
En 中文
Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer -defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.
Keywords:
Influencer and defaulter mutation
Optimization algorithms
Price prediction
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