일사량 가중치를 활용한 평일 전력수요예측 알고리즘

초록

For efficient operation of the electric power system, it is important to increase the accuracy of load forecasting. Recently, small capacity solar photovoltaic(PV) generation in South Korea is the majority of Behind-The-Meter (BTM) generation, which is one of the main factors that change the gross load. Solar radiation is a weather factor that is highly correlated with solar PV generation and closely related to BTM in South Korea. So far, solar radiation is not systematically reflected in the load forecasting algorithm, so the load forecasting error is increasing due to the BTM solar PV generation. Therefore, it is necessary to analyze the impact of the BTM and to study the load forecasting algorithm considering the BTM. As a method to reflect the impact of BTM solar PV generation on the load forecasting, the load sensitivity by cloud amount is calculated. To reflect the characteristics of solar radiation that changes monthly due to earth's revolution, the load sensitivity by cloud amount with monthly solar radiation weighting is calculated. The load sensitivity by cloud amount with monthly solar radiation weighting is applied to the weekday load forecasting, and the accuracy is better than the load forecasting result applied only load sensitivity by temperature.

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