Affiliation:
1. Techno Main Saltlake, Kolkata, India
2. Namibia University of Science and Technology, Nambia
Abstract
Time series-based forecasting is one of the most popular machine learning methods due to its effectiveness in estimation of future values based on observations, interpolations, and interpretations of past data. Forecasting the load in power distribution networks is an essential step in energy trading and system operation. Machine learning, specifically time series-based forecasting, can be used in the precise prediction of energy consumption in power networks. Precise predictions have the potential to reduce operating and maintenance expenses, enhance the dependability of power supply and delivery systems, and enable informed decisions for future development endeavors. This chapter employs time series analysis to forecast energy usage in 10-minute intervals specifically for the city of Tétouan in Morocco by applying gradient boosting algorithm. Past and present data trends have been presented along with various accuracy parameters such as mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE).
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