Li Shoujiang, Wang Jianzhou, Zhang Hui, Liang Yong
Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau, 999078 China.
School of Mathematics and Data Science, Shaanxi University of Science and Technology, Xi'an, 710021 Shaanxi China.
Appl Intell (Dordr). 2023 Jun 7:1-35. doi: 10.1007/s10489-023-04599-0.
Short-term electricity load forecasting is critical and challenging for scheduling operations and production planning in modern power management systems due to stochastic characteristics of electricity load data. Current forecasting models mainly focus on adapting to various load data to improve the accuracy of the forecasting. However, these models ignore the noise and nonstationarity of the load data, resulting in forecasting uncertainty. To address this issue, a short-term load forecasting system is proposed by combining a modified information processing technique, an advanced meta-heuristics algorithm and deep neural networks. The information processing technique utilizes a sliding fuzzy granulation method to remove noise and obtain uncertainty information from load data. Deep neural networks can capture the nonlinear characteristics of load data to obtain forecasting performance gains due to the powerful mapping capability. A novel meta-heuristics algorithm is used to optimize the weighting coefficients to reduce the contingency and improve the stability of the forecasting. Both point forecasting and interval forecasting are utilized for comprehensive forecasting evaluation of future electricity load. Several experiments demonstrate the superiority, effectiveness and stability of the proposed system by comprehensively considering multiple evaluation metrics.
由于电力负荷数据的随机特性，短期电力负荷预测对于现代电力管理系统中的调度运行和生产计划至关重要且具有挑战性。当前的预测模型主要专注于适应各种负荷数据以提高预测的准确性。然而，这些模型忽略了负荷数据的噪声和非平稳性，导致预测存在不确定性。为了解决这个问题，通过结合改进的信息处理技术、先进的元启发式算法和深度神经网络，提出了一种短期负荷预测系统。信息处理技术利用滑动模糊粒化方法去除噪声并从负荷数据中获取不确定性信息。由于强大的映射能力，深度神经网络可以捕捉负荷数据的非线性特征以获得预测性能提升。一种新颖的元启发式算法用于优化加权系数，以减少偶然性并提高预测的稳定性。点预测和区间预测都用于对未来电力负荷进行综合预测评估。几个实验通过综合考虑多个评估指标证明了所提出系统的优越性、有效性和稳定性。
