Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks

Abstract

Many electricity customers, particularly in the area of small and medium-sized enterprises, are characterized by a system of 3-phase unbalanced current consumption and the polarization of power factor in the individual phases. Strong variation of power factor in the different phases (often with both inductive and capacitive nature) causes the inability to install classic–a 3-phase reactive power compensators. Therefore, energy consumers are exposed to higher costs for the crossing of the contractual power factor. This paper describes the problem of reactive power forecasting with the use of artificial neural networks. For calculation, the Nonlinear Autoregressive (NAR) neural network was used for different input vector sizes and different numbers of neurons in the hidden layer for foreseeing reactive power generation. Results of simulations compared to real measurements confirm that it is possible to forecast the reactive power course, useful for optimal planning of reactive power compensation strategies.