Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role throughout the operation of the conventional power system. The precise modelling and complex analyses of STLF have become more significant in advanced microgrid (MG) applications. Several models are proposed for STLF and tested successfully in the literature. The selection of a forecasting method is mostly based on data availability and its objectives. This article presents a survey of the latest analytical and approximation techniques reported in the literature to model STLF in an MG environment. This article mainly focusses on the review on important methods applied to forecast renewable energy availability, energy demand, and price and load demand. Different models, their main objectives, methodology, error percentage, and so forth, are critically reviewed and analysed. For quick reference, we have highlighted the important points in the form tables. The researchers can quickly identify and frame their research problem related to the LF area by reading this review paper.

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“Short-term load forecasting (STLF)” is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forecasting model with wavelet transform for the STLF depending on the decomposition principle of load profiles. The model can effectively capture the portion of daily load profiles caused by seasonal variations. The results indicate that it is possible to improve STLF accuracy with the proposed method. The proposed approach is tested with the data taken from Ontario’s electricity market in Canada. The results show that the proposed technique performs well in-terms of prediction when compared to existing traditional and cutting-edge methods. The performance of the model was validated with different datasets. Moreover, this approach can provide accurate load forecasting using ensemble models. Therefore, utilities and smart grid operators can use this approach as an additional decision-making tool to improve their real-time decisions.

The electrical load has a prominent position and a very important role in the day-to-day operations of the entire power system. Due to this, many researchers proposed various models for forecasting load. However, these models are having issues with over-fitting and the capability of generalization. In this paper, by adopting state-of-the-art of deep learning, a modified deep residual network (deep-ResNet) is proposed to improve the precision of short-term load forecasting and overcome the above issues. In addition, the concept of statistical correlational analysis is used to identify the appropriate input features extraction ability and generalization capability in order to progress the accuracy of the model. Two utility (ISO-NE and IESO-Canada) datasets are considered for evaluating the proposed model performance. Finally, the prediction results obtained from the proposed model are promising as well as accurate when compared with the other existing models in the literature.