Electricity Supply Model of Conventional Residential Buildings in Tehran with Priority on Renewable Energy Using Adaptive Fuzzy-neural Inference System

Energy consumption in the building sector, especially in residential buildings, due to the development of urbanization, has taken the largest share among all consumption sectors. Therefore, it is very necessary to predict the energy consumption of buildings, which has been presented as a challenge in recent decades. In this research, adaptive fuzzy-neural inference system (ANFIS) and MATLAB software have been used for forecasting to supply electrical energy to residential buildings whit random data that collected based on the hourly electricity consumption of conventional residential buildings in Tehran. According to the applied settings for the solar and wind energy production has been done by solar panels and wind turbines. The use of renewable energy is one of the ways that can reduce the consumption of fossil fuels and also reduce environmental pollution. Statistical indicators  such az MSE, RMSE, µ, σ, and R were used to evaluate the model performance . The obtained values well show the ability of this model to foresee the generation and utilization of energy in privat reresidential buildings with tall exactness of about ۹۶% and ۹۰%, respectively. Therefore, this model well show the ability of to the needed estimates in the mentioned buildings with high accuracy.