With the rapid expansion of renewable energy, the penetration rate of behind-the-meter (BTM) solar photovoltaic (PV) generators is increasing in South Korea. The BTM solar PV generation is not metered in real-time, distorts the electric load and increases the errors of load forecasting. In order to overcome the problems caused by the impact of BTM solar PV generation, an extreme gradient boosting (XGBoost) load forecasting algorithm is proposed. The capacity of the BTM solar PV generators is estimated based on an investigation of the deviation of load using a grid search. The influence of external factors was considered by using the fluctuation of the load used by lighting appliances and data filtering based on base temperature, as a result, the capacity of the BTM solar PV generators is accurately estimated. The distortion of electric load is eliminated by the reconstituted load method that adds the estimated BTM solar PV generation to the electric load, and the load forecasting is conducted using the XGBoost model. Case studies are performed to demonstrate the accuracy of prediction for the proposed method. The accuracy of the proposed algorithm was improved by 21% and 29% in 2019 and 2020, respectively, compared with the MAPE of the LSTM model that does not reflect the impact of BTM solar PV.

Short-term load forecasting (STLF) is very important for planning and operating power systems and markets. Various algorithms have been developed for STLF. However, numerous utilities still apply additional correction processes, which depend on experienced professionals. In this study, an STLF algorithm that uses a similar day selection method based on reinforcement learning is proposed to substitute the dependence on an expert’s experience. The proposed algorithm consists of the selection of similar days, which is based on the reinforcement algorithm, and the STLF, which is based on an artificial neural network. The proposed similar day selection model based on the reinforcement learning algorithm is developed based on the Deep Q-Network technique, which is a value-based reinforcement learning algorithm. The proposed similar day selection model and load forecasting model are tested using the measured load and meteorological data for Korea. The proposed algorithm shows an improvement accuracy of load forecasting over previous algorithms. The proposed STLF algorithm is expected to improve the predictive accuracy of STLF because it can be applied in a complementary manner along with other load forecasting algorithms.

The increasing integration of renewable energy resources, driven by carbon neutrality goals, has intensified load variability, thereby making very short-term load forecasting (VSTLF) more challenging. Accurate VSTLF is essential for the reliable and economical real-time operation of power systems. This study proposes a Long Short-Term Memory (LSTM)-based VSTLF model designed to predict nationwide power system load, including renewable generation over a six-hour horizon with 15 min intervals. The model employs a reconstituted load approach that incorporates photovoltaic (PV) generation effects and computes representative weather variables across the country. Furthermore, the most informative input features are selected through a combination of correlation analyses. To further enhance input sequences, pseudo-trend components are generated using a Kalman filter-based predictor and integrated into the model input. The Kalman filter-based pseudo-trend produced an MAPE of 1.724%, and its inclusion in the proposed model reduced the forecasting error (MAPE) by 0.834 percentage points. Consequently, the final model achieved an MAPE of 0.890%, which is under 1% of the 94,929 MW nationwide peak load.

It is essential to forecast 240 hours load accurately for stable power system operation in South Korea. Training technique for 240 hours load forecasting using deep learning is proposed. Suitable training technique for 240 hours load forecasting is developed using deep learning model. The 240 hours load forecasting method is proposed applying training technique which consists of two different types for weather prediction. According to usable weather factors depending on forecast ranges, training technique is designed by reflecting time intervals between forecasting time and forecasting target. The proposed method has improved prediction accuracy compared to the existing exponential weighted moving average method.