Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries
نویسندگان
چکیده
Currently, developing countries are experiencing a massive shift toward industrialization. Developing lack the technical sophistication and infrastructure to encourage low-carbon sustainable economic growth because of weak public awareness, regulations, technology. must plan industrialization process for maximum energy efficiency production, thereby reducing their CO 2 emissions significantly by increasing efficiency. This paper attempts review current pragmatic methods forecasting future load demands from minutes years ahead in countries, following Preferred Reporting Items Systematic Meta-Analysis Protocols (PRISMA-P). Our primary focus is provide an optimal model selection strategy potential researchers forecasters. Based on strengths weaknesses different models, we will discuss most suitable tailor them multiple applications scenarios forecasting. The comparison elements Forecast horizons, Spatio-temporal resolutions, factors affecting load, dimensional reduction techniques, complexity analysis, MAPE error analysis. From results, We have found ANN hybridized with meta-heuristic techniques be superior analysis cases. ANN’s ability handle non-linear data, flexibility, robustness why. Consumption data aggregated at national level can capture trends efficiently. Meteorological calendar features influence short-term extensively, whereas long-term patterns. Finally, identified research gaps existing literature, presenting relevant recommendations improvement.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2023
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2023.3250110