Prediction of wind and solar energy is deemed one of the most important contributory factors towards sustainability. Along the same lines, to harvest energy and guarantee the safety of a place, accurate information about the future of the region is needed. To attain this goal, this paper predicts solar irradiation and wind velocity time series by two robust artificial intelligence algorithms which are called wavelet neural network and ANFIS (Adaptive Network Fuzzy Inference System). The data used for the predictor system are obtained from a meteorological station in Tehran, Iran. The results show that robustness of both algorithms for prediction of wind velocities and solar irradiation and superior strength of wavelet neural network (WNN) to ANFIS for prediction of solar irradiation and wind velocities.

In the current century, energy is become as one of the most critical issues in human's life. Due to global warming, air pollution and the other problems caused by fossil fuels, one of the appropriate sources which is renewable and is invested is wind energy. Iran has a good potential to use wind energy based on its geographical features. Therefore, to have the best productivity to employ wind energy, location of farm winds in a suitable site is a delicate issue. This research applies a hybrid MCDM method for prioritizing potential cities in Iran to install wind farms. In this regard, Step-wise Weight Assessment Ratio Analysis (SWARA) is employed to rank criteria and Weighted Aggregates Sum Product Assessment (WASPAS) is utilized to evaluate alternatives. In this study 68 cities are detected as high potential places for this aim. Eventually, the most appropriate city is identified as the best place to install wind farms. The results of this research draw a OPEN ACCESS 2 conclusion for decision making and planning of energy management in top level of managing requirements of countries in all aspects.