Prediction system based on domotic weather sensors for the energy production of solar power plants

Abstract

The prediction of the electrical energy generated by a photovoltaic system is useful for estimating the profitability analysis of a project, without the need of expensive photovoltaic prototypes. Prediction systems are usually based on simulating the physical process of a photovoltaic module, under standard or average local weather conditions. These predictions introduce some errors caused by the use of a theoretical model or average climate data. In our investigations, we noted that the energy generated by a photovoltaic system is proportional to the cumulative measurement of the sun illuminance that is provided by a low-cost domotic weather station. From this experimental observation, this paper proposes a hardware/software system for predicting the electrical energy generated by a photovoltaic system, such as those existing in buildings. The hardware consists of a domotic installation for monitoring both electric energy and climate parameters. The software consists of a calibration procedure, which provides a proportional factor between sun illuminance and the energy production per unit of surface area of the photovoltaic modules. Once the calibration procedure is completed, the photovoltaic energy production is predicted by factoring the sun illuminance provided by the weather station and the proportional factor provided by the calibration process. This method has been tested under real conditions and the accuracy reached up to 99.7% with an average value of 96.3%.