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.