  Accurate load forecasting is critical for efficient and reliable operations
of the electric power system. A large part of electricity consumption is
affected by weather conditions, making weather information an important
determinant of electricity usage. Personal appliances and industry equipment
also contribute significantly to electricity demand with temporal patterns,
making time a useful factor to consider in load forecasting. This work develops
several machine learning (ML) models that take various time and weather
information as part of the input features to predict the short-term system-wide
total load. Ablation studies were also performed to investigate and compare the
impacts of different weather factors on the prediction accuracy. Actual load
and historical weather data for the same region were processed and then used to
train the ML models. It is interesting to observe that using all available
features, each of which may be correlated to the load, is unlikely to achieve
the best forecasting performance; features with redundancy may even decrease
the inference capabilities of ML models. This indicates the importance of
feature selection for ML models. Overall, case studies demonstrated the
effectiveness of ML models trained with different weather and time input
features for ERCOT load forecasting.
