Load Forecasting Techniques for Power System: Research Challenges and Survey
Abstract
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy and small
error to give uninterrupted and free of load shedding power to consumers. The demand of electricity can be
forecasted amicably by many Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI)
techniques among which hybrid methods are most popular. The present technologies of load forecasting and
present work regarding combination of various ML, DL and AI algorithms are reviewed in this paper. The
comprehensive review of single and hybrid forecasting models with functions; advantages and disadvantages
are discussed in this paper. The comparison between the performance of the models in terms of Mean
Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE)
values are compared and discussed with literature of different models to support the researchers to select the
best model for load prediction. This comparison validates the fact that the hybrid forecasting models will
provide a more optimal solution.
Author
Ahmad, Naqash
Ghadi, Yazeed
Adnan, Muhammad
Ali, Mansoop