IMPROVING THE ACCURACY OF SHORT-TERM LOAD FORECASTING OF DELIVERY POINT CLUSTER OF THE SECOND LEVEL DEFAULT PROVIDER

• Main Authors: Stanislav O. Khomutov, Vasiliy I. Stashko, Nikolay A. Serebryakov
• Format: Article
• Language: Russian
• Published: Tomsk Polytechnic University 2020-06-01
• Series: Известия Томского политехнического университета: Инжиниринг георесурсов
• Subjects: short-term load forecasting; artificial neural networks; learning algorithm; wholesale electricity market; delivery point cluster; gradient descent
• Online Access: http://izvestiya.tpu.ru/archive/article/view/2682/2248
• ISSN: 2500-1019; 2413-1830

Relevance of the discussed issue is caused by the need to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider. The system operator uses the result of forecast when forming power system dispatch load curve. Usually, prediction errors lead to increase of primary energy resources consumption for electric-power production, due to unjustified run and shutdown of generating equipment, as well as increasing of circuit losses, due to the choice of non-optimal scheme of electric grid. As the electricity consumption depends on many factors, the task of short-term load forecasting is poorly formalized. Under these conditions, traditional methods of mathematical statistics and simulation do not allow building the adequate forecast models. Until recently, the only fine method of load forecasting was the Delhi approach. Currently, tools of neural networks and deep machine learning are widely used for short-term load forecasting of the energy system of a region of the country or delivery point cluster of first level default provider. However, the developed models are not suitable for predicting hourly electricity consumption of delivery point cluster of the second level default provider. Short-term load forecasting of this object is complicated of reliability of electric grid 6–110 kV, the operating mode of electricity consumers with a capacity of 670–10000 kW, the presence of district heating and water supply, beside standard time and meteorological factors. For this forecasting object, the question of choosing the optimal architecture and configuration of the neural network model, as well as the learning algorithm, which can achieve the desired forecasting accuracy, remain open. The main aim of the research is to improve the accuracy of short-term load forecasting of delivery point cluster of the second level default provider with the help of tools of neural networks and deep machine learning. The methods: the methods of correlation and factor analysis, the theory of artificial neural networks and machine learning. Software implementation of theoretical calculations was performed with help of deep machine learning library Tensor flow Keras in the Python 3.6 programming language. Results. The authors have developed the neural network algorithm for short-term load forecasting of delivery point cluster of the second level default provider with adaptive learning and momentum rate and completed the software implementation of this algorithm in deep machine learning library Tensor flow Keras. The use of this artificial neural network let to decrease in monthly average relative forecast error by 5,14 %.