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Accurate load forecasting is critical for efficient and reliable operationsof the electric power system. A large part of electricity consumption isaffected by weather conditions, making weather information an importantdeterminant of electricity usage. Personal appliances and industry equipmentalso contribute significantly to electricity demand with temporal patterns,making time a useful factor to consider in load forecasting. This work developsseveral machine learning (ML) models that take various time and weatherinformation as part of the input features to predict the short-term system-widetotal load. Ablation studies were also performed to investigate and compare theimpacts of different weather factors on the prediction accuracy. Actual loadand historical weather data for the same region were processed and then used totrain the ML models. It is interesting to observe that using all availablefeatures, each of which may be correlated to the load, is unlikely to achievethe best forecasting performance; features with redundancy may even decreasethe inference capabilities of ML models. This indicates the importance offeature selection for ML models. Overall, case studies demonstrated theeffectiveness of ML models trained with different weather and time inputfeatures for ERCOT load forecasting.

Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting