@article{scholars15642,
         journal = {IEEE Access},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           pages = {142239--142263},
            year = {2021},
           title = {Electrical Load Forecasting Models for Different Generation Modalities: A Review},
          volume = {9},
            note = {cited By 21},
             doi = {10.1109/ACCESS.2021.3120731},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117783837&doi=10.1109\%2fACCESS.2021.3120731&partnerID=40&md5=c4df37d4a6577baf7556289a7596ae01},
        keywords = {Electric load flow; Electric power transmission networks; Errors; Forecasting; Learning systems; Smart power grids, Data analytics; Electrical generation; Electrical generation modality; Grid data; Grid data-analytic; Isolated grids; Isolated-grid load forecasting; Load forecasting; Power plant load forecasting; Smart grid, Electric power plant loads},
        abstract = {The intelligent management of power in electrical utilities depends on the high significance of load forecasting models. Since the industries are digitalized, power generation is supported by a variety of resources. Therefore, the forecasting accuracy of different models varies. The power utilities with different generation modalities (DGM) experience complexities and a noticeable amount of error in predicting future electrical consumption. To effectively manage the power flow with negligible power interruptions, a utility must utilize the forecasting tools to predict the future electricity demand with minimum error. Since the current literature supports individual and limited power sources involved in generation for load forecasting, thus the utilities with multiple power sources or DGM remain unexplored. Therefore, exploration of existing literature is required relating to analyzing the existing models which could be considered in load forecasting for DGM. This paper explores state-of-art methods recently utilized for electrical load forecasting highlighting the common practices, recent advances, and exposure of areas available for improvement. The review investigates the methods, parameters, and respective sectors considered for load forecasting. It performs in-depth analysis and discusses the strengths, weaknesses, and error percentages of models. It also highlights the peculiarities of methods used in residential, commercial, industrial, grid, and off-grid sectors aiming to help the researchers to appraise the common practices. Moreover, trends and research gaps are also discussed. {\^A}{\copyright} 2013 IEEE.},
          author = {Azeem, A. and Ismail, I. and Jameel, S. M. and Harindran, V. R.},
            issn = {21693536}
}