Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems

Abstract

Predicting the variability of wind energy resources at different time scales is extremely important for effective energy management. The need to obtain the most accurate forecast of wind speed due to its high degree of volatility is particularly acute since this can significantly improve the planning of wind energy production, reduce costs and improve the use of resources. In this study, a method for predicting the speed of wind flow in an isolated power system of the Gorno-Badakhshan Autonomous Oblast (GBAO), based on the use of a neural network with a learning process control algorithm, is proposed. Predicting is performed for four seasons of the year, based on hourly retrospective meteorological data of wind speed observations. The obtained wind speed average error forecasting ranged from 20–28% for a day ahead. The prediction results serve as a basis for optimizing the energy consumption of individual generating consumers to minimize their financial and technical costs. In addition, this study takes into account the possibility of exporting electricity to a neighboring country as an additional income line for the isolated GBAO power system during periods of excess energy from hydropower plants (March–September), which is a systematic vision of solving the problem of improving energy efficiency in the conditions of autonomous power supply.

Short-Term Solar Insolation Forecasting in Isolated Hybrid Power Systems Using Neural Networks

“…The solar energy potential indicators for the GBAO are depicted in Figure 1. The proximity of energy consumers allows for the installation of solar panels without the need for extensive power line infrastructure, presenting a significant advantage for solar energy in the GBAO [19]. The proximity of energy consumers allows for the installation of solar panels without the need for extensive power line infrastructure, presenting a significant advantage for solar energy in the GBAO [19].…”

“…In this solution, a neural network utilizes a multilayer perceptron with one hidden layer as its fundamental architecture [19]. The two different model variants are considered:…”

“…In this region, the unpredictability of the energy generation from renewable and alternative sources (including small mountain river-based hydropower plants (HPPs), solar power plants (SPPs), and wind power plants (WPPs) combined with energy storage devices) poses a significant challenge to maintaining a stable electric energy balance. Therefore, the problem addressed in this study holds substantial relevance [18,19]. The problem of the control of the autonomous power system of GBAO is described in [18].…”

“…The problem of the control of the autonomous power system of GBAO is described in [18]. The problem of the short-term forecasting of wind speed, which is necessary for forecasting generation at WPPs, is considered in [19].…”

Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables

“…Liu et al [13] studied neural network combined models and proposed a hybrid method for wind speed forecasting. The adaptation of neural networks for the short-term forecasting of wind speed was studied in [14]. Furthermore, a hybrid evolutionary optimised ANFIS was proposed for the same task in [15], together with singular spectrum analysis.…”