Abstract
Estimating the electricity load is a crucial task in the planning of power generation systems and the efficient operation and sustainable growth of modern electricity supply networks. Especially with the advent of smart grids, the need for fairly precise and highly reliable estimation of electricity load is greater than ever. It is a challenging task to estimate the electricity load with high precision. Many energy demand management methods are used to estimate future energy demands correctly. Machine learning methods are well adapted to the nature of the electrical load, as they can model complicated nonlinear connections through a learning process containing historical data patterns. Many scientists have used machine learning (ML) to anticipate failure before it occurs as well as predict the outcome. ML is an artificial intelligence (AI) subdomain that involves studying and developing mathematical algorithms to understand data or obtain data directly without relying on a prearranged model algorithm. ML is applied in all industries. In this paper, machine learning strategies including artificial neural network (ANN), multiple linear regression (MLR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) were used to estimate electricity demand and propose criteria for power generation in Cyprus. The simulations were adapted to real historical data explaining the electricity usage in 2016 and 2107 with long-term and short-term analysis. It was observed that electricity load is a result of temperature, humidity, solar irradiation, population, gross national income (GNI) per capita, and the electricity price per kilowatt-hour, which provide input parameters for the ML algorithms. Using electricity load data from Cyprus, the performance of the ML algorithms was thoroughly evaluated. The results of long-term and short-term studies show that SVM and ANN are comparatively superior to other ML methods, providing more reliable and precise outcomes in terms of fewer estimation errors for Cyprus’s time series forecasting criteria for power generation.
Highlights
Energy is vital for the sustainable development of any country
The aim of this paper is to develop mathematical models for energy forecasting in Cyprus and comparing the performance of the models for long term and short term forecasting
Precise load forecasting is crucial for the planning of power systems and operational decision making
Summary
Energy is vital for the sustainable development of any country. Accurate energy forecasting is crucial for sustainable economic prosperity and environmental security. Energy demand forecasting is required for the proper allocation of available resources. Several new techniques have been used for energy forecasting to accurately predict future energy needs. Energy demand management involves effective utilization and management of energy resources, reliability of the supply, energy conservation, combined heat and power systems, renewable and integrated energy systems, independent power delivery systems, etc. Demand management has to consider a series of technical, organizational, or behavioral solutions to decrease energy consumption and demand. Cost-effective options, commercially viable alternatives, and environmentally friendly solutions need to be explored
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