A Review on Machine Learning and Deep Learning Models for Electrical Load Forecasting

Data driven machine learning models happen to effectively address the continuous mismatch between the amount of generation and the load required for power systems. The necessity for matching of load is necessary as lesser load than generation may lead to standing waves on the transmission line and power feedback paths. While the excessive load may put enormous amount of pressure on the generating end with limited generation capability. Hence, it is necessary to match the demand and load conditions for any power system. This inevitable needs accurate forecasting models for electrical load so as to make estimates regarding the future load conditions of any system. While electrical load forecasting a time series forecasting model, the dependence on the multitude of parameters makes the load forecasting a challenging problem. This paper presents a review on the current techniques for electrical load forecasting based on statistical regression problems so as to enhance the balance among the electrical load required and the generation.