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The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

In hot and dry climates, air conditioning accounts for a large portion of total energy consumption; therefore, this paper aims to investigate the impact of sol-air temperature and ground temperature on the loss of cooling energy in hot and dry regions of Iran.

In line with this objective, the values of sol-air temperature along different directions and ground temperature at different depths were assessed with respect to climatic data of Yazd City. The impact of sol-air temperature and ground temperature on the rate of heat loss was investigated. So, energy loss of the walls aligned to four primary directions was calculated. This process was repeated for a 36 m² building with three different shape factors. All analyses were conducted for the period from May to September, during which buildings need to be cooled by air conditioners.

Numerical analyses conducted for hot and dry climate show that sol-air temperature leads to a 41-17 per cent increase in the wall’s energy loss compared with ambient temperature. Meanwhile, building the wall below the surface leads to a significant reduction in energy loss. For example, building the wall 400 cm below the surface leads to about 74.8-79.2 per cent energy saving compared with above ground design. The results also show that increasing the direct contact between soil and building envelope decreases the energy loss, so energy loss of a building that is built 400 cm below the surface is 53.7-55.3 per cent lower than that of a building built above the surface.

The impact of sol-air temperature and ground temperature on the cooling energy loss of a building in hot and dry climate was investigated. Numerical analysis shows that solar radiation increases heat loss from building envelope. Soil temperature fluctuations decrease with depth. Heat loss from building envelope in an underground building is lower than that from building envelope in a building built above the ground. Three different shape factors showed that sol-air temperature has the maximum impact on square-shaped plan and minimal impact on buildings with east-west orientation.