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By reducing fossil fuel resources, power system operators have interested in renewable energy sources. On the other hand, energy consumption in buildings is included about 40 percent of all energy used in the world. Thus, the researchers have always been seeking a solution to reduce power consumption in the building. This has been led to introduction of the concept of zero-energy buildings. Zero energy buildings refers to buildings that have zero or positive energy consumption in one year. The plane of building houses with zero energy consumption is based on using the household sector from renewable energies. In this study, solar energy and wind energy among renewable sources are selected to meet the power requirements in buildings. The output power level of renewable sources is uncertain and is constantly changing due to the fluctuating nature of wind speed and solar radiation .So in this study, first, the method is provided to forecast output power sources by the neural network algorithm. To utilize a building in dependently, renewable resources should be available with sufficient capacity and energy storage such as batteries, should be available to cover the time periods when renewable resources are not able to produce power. Genetic algorithm will be used to solve the optimization problem. Therefore, in this study, considering to the cost and the least certain amount of load as objective functions system, production planning and consumption in buildings are implemented based on the DGs as a multi-function and the optimized capacity of photovoltaic panels and wind turbines will be determined.

Multi-objective optimization to have optimal utilization from renewable energy sources in building with zero energy