随着信息技术的快速发展,信息化日益成为推动企业发展的重要力量。电力企业作为能源供应的重要组成部分,也在逐步实现信息化管理,其中用电量预测显得尤为关键。通过深入分析历史数据和各类相关因素,发现用电量与周期性变化、节假日、温...随着信息技术的快速发展,信息化日益成为推动企业发展的重要力量。电力企业作为能源供应的重要组成部分,也在逐步实现信息化管理,其中用电量预测显得尤为关键。通过深入分析历史数据和各类相关因素,发现用电量与周期性变化、节假日、温度等特征之间具有很强的关联性。为此,针对区域中长期用电量预测,建立了融合多变量与季节效应的ARIMAX模型,可预测未来半年的用电量。针对区域短期用电量,则采用LSTM模型并优化其参数,实现对未来两周用电量的精准预测。与原始ARIMA等模型相比,该方法展现出更优越的预测性能,不仅为电网运行安全提供可靠的理论支撑,还为电力企业在信息化管理、资源优化及决策制定方面提供了重要参考。With the rapid development of information technology, informatization has increasingly become an important force driving the development of enterprises. As an important component of energy supply, power enterprises are gradually implementing information management, among which electricity consumption prediction is particularly crucial. Through in-depth analysis of historical data and various related factors, it was found that there is a strong correlation between electricity consumption and characteristics such as periodic changes, holidays, and temperature. For this purpose, an ARIMAX model integrating multiple variables and seasonal effects was established for long-term electricity consumption prediction in the region, which can predict electricity consumption for the next six months. For short-term electricity consumption in the region, the LSTM model is adopted and its parameters are optimized to achieve accurate prediction of electricity consumption for the next two weeks. Compared with the original ARIMA and other models, this method demonstrates superior predictive performance, not only providing reliable theoretical support for the safe operation of the power grid, but also providing important references for power enterprises in information management, resource optimization, and decision-making.展开更多
对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性...对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性,其计算结果更为可靠,在实际系统中的可解释性也更强,且方法简单,计算快捷。该文将偏最小二乘回归模型(Partial Least Square Regression,PLS)应用于年用电量预测,并与基于最小二乘的多元线性回归模型预测成果进行对比,探讨了偏最小二乘法在电力负荷预测中的可行性和优势。通过四川省电网年用电量预测表明:偏最小二乘回归法比一般最小二乘法优,具有较强的实用性。展开更多
文摘随着信息技术的快速发展,信息化日益成为推动企业发展的重要力量。电力企业作为能源供应的重要组成部分,也在逐步实现信息化管理,其中用电量预测显得尤为关键。通过深入分析历史数据和各类相关因素,发现用电量与周期性变化、节假日、温度等特征之间具有很强的关联性。为此,针对区域中长期用电量预测,建立了融合多变量与季节效应的ARIMAX模型,可预测未来半年的用电量。针对区域短期用电量,则采用LSTM模型并优化其参数,实现对未来两周用电量的精准预测。与原始ARIMA等模型相比,该方法展现出更优越的预测性能,不仅为电网运行安全提供可靠的理论支撑,还为电力企业在信息化管理、资源优化及决策制定方面提供了重要参考。With the rapid development of information technology, informatization has increasingly become an important force driving the development of enterprises. As an important component of energy supply, power enterprises are gradually implementing information management, among which electricity consumption prediction is particularly crucial. Through in-depth analysis of historical data and various related factors, it was found that there is a strong correlation between electricity consumption and characteristics such as periodic changes, holidays, and temperature. For this purpose, an ARIMAX model integrating multiple variables and seasonal effects was established for long-term electricity consumption prediction in the region, which can predict electricity consumption for the next six months. For short-term electricity consumption in the region, the LSTM model is adopted and its parameters are optimized to achieve accurate prediction of electricity consumption for the next two weeks. Compared with the original ARIMA and other models, this method demonstrates superior predictive performance, not only providing reliable theoretical support for the safe operation of the power grid, but also providing important references for power enterprises in information management, resource optimization, and decision-making.
文摘对年用电量的预测若采用一般最小二乘回归法建模,其估计参数存在着很大的误差且物理意义明显不足。而偏最小二乘回归方法则实现了多元线性回归、主成分分析和典型相关分析的综合、克服了自变量之间的多重相关性的问题,因而更具有先进性,其计算结果更为可靠,在实际系统中的可解释性也更强,且方法简单,计算快捷。该文将偏最小二乘回归模型(Partial Least Square Regression,PLS)应用于年用电量预测,并与基于最小二乘的多元线性回归模型预测成果进行对比,探讨了偏最小二乘法在电力负荷预测中的可行性和优势。通过四川省电网年用电量预测表明:偏最小二乘回归法比一般最小二乘法优,具有较强的实用性。
