Medium-Term Forecasting of Power Consumption by ANN Considering Meteo-Factors

The main task of forecasting power consumption is to analyze objective factors affecting load changes and compute expected schedules of consumption to support energy companies organize the purchase and production of the necessary electricity in the proper volume. However, modern practical requirements for the accuracy of predictive calculations lead to the fact that previously developed methods do not always provide the necessary accuracy of computations. This is because several factors affect the level of power consumption and the accuracy of forecasting, including meteorological, where the growing use of both wind and photovoltaic energy worldwide leads the power systems to become highly dependent on the weather conditions. This study presents the influence of meteorological factors on the medium-term forecast of electricity consumption based on the power system of the Gorno-Badakhshan Autonomous Oblast in the Republic of Tajikistan, where in winter, due to an increase in demand for electricity and a drop in the water level in rivers, to ensure balance, requires additional commissioning of diesel power plants. Thus, it leads to an increase in the costs of electricity production in the region and environmental degradation. To increase the forecasting effectiveness, the authors propose an approach based on the clustering of meteorological conditions, where each cluster has its regression model. The Principal Component Analysis was proposed for aggregating meteorological factors.