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Quantitative combination load forecasting model based on forecasting error optimization
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PRE
AI
DOI：10.1016/j.compeleceng.2022.108125.png)
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Abstract
En 中文
Accurate load forecasting is indispensable in various applications of the electric power industry. Although existing load forecasting methods perform well, they cannot handle complicated scenarios where load-related data are highly random and uncertain. To deal with this issue, A Quantitative Combination Load Forecasting model(QCLF) is proposed. Its main idea is to incorporate the load forecasting errors into the forecasting process as an optimization problem, which can significantly reduce the adverse impacts of random and uncertain load-related data. First, we propose an improved K-Means and Least Square-based Load Forecasting Error Model(LFEM-KLS) to improve the availability and effectiveness of load-related data. Second, we employ gene expression programming (GEP) to optimize the proposed LFEM-KLS to achieve highly accurate load forecasting. Experimental results on three load datasets demonstrate that a QCLF model significantly outperforms other related load forecasting models.
Keywords:
Gene expression programming
Data noise reduction
Load forecasting error
Combination load forecasting
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