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In this paper, four probability functions are compared with the purpose of establishing a methodology to improve the accuracy of wind energy estimations in a desert city in Northwestern Mexico. Three time series of wind speed data corresponding to 2017, 2018, and 2019 were used for statistical modeling. These series were recorded with a sonic anemometer at a sampling frequency of 10 Hz. Analyses based on these data were performed at different stationarity periods (5, 30, 60, and 600 s). The estimation of the parameters characterizing the probability density functions (PDFs) was carried out using different methods; the statistical models were evaluated by the coefficient of determination and Nash–Sutcliffe efficiency coefficient, and their accuracy was estimated by the measured quadratic error, mean square error, mean absolute error, and mean absolute percentage error. Weibull, using the energy pattern factor method, and Gamma, using the Method of Moments, were the probability density functions that best described the statistical behavior of wind speed and were better at estimating the generated energy. We conclude that the proposed methodology will increase the confidence of both wind speed estimation and the energy supplied by small-scale wind installations.

Reducing household energy consumption is one of the most important strategies used to decrease fossil fuel consumption and greenhouse gases emissions, and to encourage renewable energy utilization. Most energy conservation strategies in the domestic sector are aimed at preferential loans, i.e., purchasing renewable electricity or to improve the efficiency of home appliances, such as air conditioning and lighting. However, despite the relative economic successes of these technologies, they have not had expected impacts in regard to energy consumption. In this work, the authors analyzed the consumption patterns of two equivalent households—one was adapted with improved thermal insulation and a 1.2 kW photovoltaic system to reduce consumption from the electrical grid. The results show that dwellings where no improvements were made registered lower electric energy consumption, due the fact that users were aware that no strategy had been implemented, and its consumption; hence, electricity payments depended solely on one’s attention over the electronic device operations. On the other hand, energy conservation strategies in households promotes confident and relaxed attitudes toward the use of energy, leading to lower energy billings, but a higher gross energy consumption.