Increasing the quality of short-term load forecasting of the delivery point cluster of agricultural producers with a learning machine
Currently, the problem of power consumption forecasting has become much more urgent. The accuracy of the short-term load forecasting depends on the financial results of the wholesale electricity market members and the power system safety. It is necessary to consider many factors for forecasting of hourly electric load time series with high accuracy. Therefore, this task is non-formalizable. The modern level of information technology allows the use of artificial neural networks (ANN) for solution such problems. This article deals with the problem of improving the quality of short-term load forecasting of the delivery point cluster of a guaranteeing provider including large agricultural producers with the help of ANN. The questions of choosing the learning paradigm, an optimal architecture and a learning algorithm of the neural network are considered. The optimal number and size of hidden layers of a multilayer perceptron is determined by means of the geometric pyramid rule. The influence of the learning rate and the momentum rate on the neural network ability to search a global minimum on the error surface in the space of free parameters is investigated. The use of adaptive learning and momentum rate parameters in the learning process has been justified. The main techniques of overcoming the overtraining of neural networks are considered. An increase in the computational efficiency of the ANN ensemble is analyzed. The problems of coding the factor space, as well as preliminary processing and scaling of training and testing data are considered. The practical implementation of ANNs of various architectures and configurations with the help of the machine learning library in Python 3.6 is considered. A comparative analysis of the accuracy of short-term load forecasting of a delivery point cluster, which is obtained with help of neural network and the Delphi method, is made.