Energy Demand Forecasting and Optimizing Electric Systems for Developing Countries

Abstract

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

Keywords

• Electricity load forecasting
• systematic review
• energy demand forecasting
• artificial neural network
• developing countries
• machine learning