Using Prophet Algorithm for Pattern Recognition and Short Term Forecasting of Load Demand Based on Seasonality and Exogenous Features
2020 52nd North American Power Symposium (NAPS), 2021
As smart meters have proliferated in recent years, electrical power companies are dealing with a ... more As smart meters have proliferated in recent years, electrical power companies are dealing with a large volume of data, known as Big Data. Consistent with this issue, data science techniques are necessary to extract the patterns of consumption and forecast load at a specific area. Short-term load forecasting (STLF) models are crucial for electric utilities and play a vital role in generation planning, system operation, system security (false data detection), energy trading (buying/purchasing), reconfiguration, and contingency analysis. Nevertheless, electric consumption forecasting is challenging due to uncertainty incorporated in consumers' behavior. In this paper, we propose a comprehensive two-stage STLF method based on the Prophet Algorithm (PA). In the first stage and after data mining, load consumption patterns are extracted based on trend and seasonality features (e.g., spring, summer, autumn, winter, daily, weekly, etc.) along with holiday effects. Also, to make the model...
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Papers by C. Hatziadoniu