考虑光伏电源间歇性与波动性的超短期电力负荷预测

温卓衡; 唐忠

温卓衡,唐忠.考虑光伏电源间歇性与波动性的超短期电力负荷预测[J].电测与仪表,2026,63(9):96-106.
Wen Zhuoheng,Tang Zhong.Ultra-short-term power load forecasting considering intermittency and volatility of photovoltaic power sources[J].Electrical Measurement & Instrumentation,2026,63(9):96-106.

考虑光伏电源间歇性与波动性的超短期电力负荷预测

Ultra-short-term power load forecasting considering intermittency and volatility of photovoltaic power sources

DOI：10.19753/j.issn1001-1390.2026.09.010

中文关键词: 光伏电源间歇性波动性超短期电力负荷负荷预测

英文关键词:photovoltaic power supply, intermittent, volatility, ultra-short-term power load, load forecasting

基金项目:国家自然科学基金（52177185）

作者中文名	作者英文名	单位
温卓衡	Wen Zhuoheng	上海电力大学电气工程学院
唐忠	Tang Zhong	上海电力大学电气工程学院

摘要点击次数: 77

中文摘要:

光伏电源的出力易受天气、光照强度等因素的影响，具有显著的间歇性与波动性。当光伏并网容量较大时，这种特性会使电力负荷呈现出非平稳随机性，进而增加了负荷预测的难度。传统的负荷预测方法由于忽略了光伏电源的间歇性与波动性，在处理复杂的负荷时间序列时，难以准确计算相关系数，导致预测偏差较大。为解决这一问题，文中提出了一种考虑光伏电源间歇性与波动性的超短期电力负荷预测方法。该方法首先采集电力负荷的历史数据，从中挖掘负荷的变化规律；同时收集光伏电源的实时工作数据，计算出间歇系数与波动系数，以明确这些系数与电力负荷之间的影响关系。根据超短期预测时间节点与当前节点的时间间隔，生成电力负荷的初始预测值，并利用上述影响参数的计算结果对初始预测值进行修正，从而得到精准的预测结果。性能测试实验表明，在普通日和休息日两种工况下，该方法得到的预测值与电力负荷实际值高度吻合，具有明显的应用优势。

英文摘要:

The output power of photovoltaic (PV) power sources is significantly affected by factors such as weather and light intensity, exhibiting notable intermittency and volatility. When the grid-connected capacity of PV is large, these characteristics cause the power load to exhibit non-stationary random characteristics, thereby increasing the difficulty of load forecasting. Traditional load forecasting methods neglect the intermittency and volatility of PV power sources. As a result, when dealing with complex load time series, they struggle to accurately calculate the correlation coefficients, leading to large forecasting errors. To address this issue, this paper proposes an ultra-short-term power load forecasting method that takes into account the intermittency and volatility of PV power sources. Historical power load data is collected to extract the load change patterns. Meanwhile, the real-time operating data of PV power sources is gathered to calculate the intermittency coefficient and volatility coefficient, and to clarify the influence relationship between these coefficients and the power load. Then, based on the time interval between the ultra-short-term forecasting time node and the current node, an initial forecast value of the power load is generated. The initial forecast value is corrected using the calculation results of the above-mentioned influence parameters to obtain an accurate forecast result. Performance test experiments demonstrate that under the two operating conditions of normal days and rest days, the forecast values obtained by this method are highly consistent with the actual power load values, demonstrating obvious application advantages.

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