Artificial intelligence application for the performance prediction of a clean energy community

Artificial Neural Networks (ANNs) are proposed for sizing and simulating a clean energy community (CEC) that employs a PV-wind hybrid system, coupled with energy storage systems and electric vehicle charging stations, to meet the building district energy demand. The first ANN is used to forecast the energy performance indicators, which are satisfied load fraction and the utilization factor of the energy generated, while the second ANN is used to estimate the grid energy indication factor. ANNs are trained with a very large database in any climatic conditions and for a flexible power system configuration and varying electrical loads. They directly predict the yearly CEC energy performance without performing any system dynamic simulations using sophisticated models of each CEC component. Only eight dimen-sionless input parameters are required, such as the fractions of wind and battery power installed, yearly mean and standard deviation values of the total horizontal solar radiation, wind speed, air temperature and load. The Garson algorithm was applied for the evaluation of each input influence on each output. Optimized ANNs are composed of a single hidden layer with twenty neurons, which leads to a very high prediction accuracy of CECs which are different from those used in ANN training. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

Artificial Neural Networks are used to size and simulate a clean energy community employing a PV-wind hybrid system, energy storage systems, and electric vehicle charging stations. The ANNs are trained with a large database to accurately predict energy performance indicators without the need for system dynamic simulations.