A survey on hyperparameters optimization algorithms of forecasting models in smart grid

Forecasting in the smart grid (SG) plays a vital role in maintaining the balance between demand and supply of electricity, efficient energy management, better planning of energy generation units and renewable energy sources and their dispatching and scheduling. Existing forecasting models are being used and new models are developed for a wide range of SG applications. These algorithms have hyperparameters which need to be optimized carefully before forecasting. The optimized values of these algorithms increase the forecasting accuracy up to a significant level. In this paper, we present a brief literature review of forecasting models and the optimization methods used to tune their hyperparameters. In addition, we have also discussed the data preprocessing methods. A comparative analysis of these forecasting models, according to their hyperparameter optimization, error methods and preprocessing methods, is also presented. Besides, we have critically analyzed the existing optimization and data preprocessing models and highlighted the important findings. A survey of existing survey papers is also presented and their recency score is computed based on the number of recent papers reviewed them. By recent, we mean that the year in which a survey paper is published and its previous three years. Finally, future research directions are discussed in detail.