Development of an Intelligent System for the Prognostication of Energy Produced by Photovoltaic Cells in Smart Grid Systems

Abstract

Solution of the problem of prognostication of the generated energy was proposed on the basis of mathematical apparatus of neural-fuzzy networks. The conceptual model of the household information system as a part of the common SMART GRID system was proposed. The main task of this system is continuous monitoring of the power net, prognostication of consumption of the energy consumed by domestic appliances and the energy produced by photovoltaic cell panels. Current and predicted data were obtained based on the use of current sensors and mathematical apparatus of neural-fuzzy logic. Importance and necessity of using SMART GRID technology for improving efficiency of power net operation was shown. Application of such systems can reduce energy costs and the environmental impact of energy systems. This effect is achieved by prognostication of the energy generated by domestic renewable energy sources, in particular photovoltaic cell panels which ensures more efficient energy management.Also, the proposed model of the information system makes it possible to account produced and consumed energy which enables creation of an energy-efficient operation schedule of household appliances. Analysis of the dependence of the forecast accuracy on the choice of input characteristics was made. As a result, the optimal number of neurons in the inner layer was empirically set to 250 with a prediction error within 5 %. Influence of weather factors on accuracy of the resulting forecasts was considered. In particular, it has been found that quite significant differences between actual and projected data (up to 12 %) are due to the inaccuracy of local forecasts. The proposed information model can be integrated into existing or designed systems of the Smart Home type