Development of an intelligent system for the prognostication of energy produced by photovoltaic cells in smart grid systems
Autor: | Irina Vdovychenko, Dennis Kuznetsov, Ivan Muzyka, Andreу Kupin |
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Rok vydání: | 2017 |
Předmět: |
Schedule
Engineering 010504 meteorology & atmospheric sciences 020209 energy Energy Engineering and Power Technology 02 engineering and technology 01 natural sciences Industrial and Manufacturing Engineering Home automation Management of Technology and Innovation 0202 electrical engineering electronic engineering information engineering Information system Electrical and Electronic Engineering Simulation 0105 earth and related environmental sciences business.industry Applied Mathematics Mechanical Engineering Photovoltaic system Computer Science Applications Renewable energy Reliability engineering Smart grid Control and Systems Engineering Microgrid business Efficient energy use |
Zdroj: | Eastern-European Journal of Enterprise Technologies. 5:4-9 |
ISSN: | 1729-4061 1729-3774 |
Popis: | 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. |
Databáze: | OpenAIRE |
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