This paper appraises the accuracy of methods for calculating wind power density (WPD), by comparing measurement values to the shape and scale parameters of the Weibull distribution (WD). For the estimation of WD parameters, the Graphical method (GP), Empirical method of Justus (EMJ), Empirical method of Lysen (EML), Energy pattern factor method (EPF), and Maximum likelihood method (ML) are used. The accuracy of each method was evaluated via multiple metrics: Mean absolute bias error (MABE), Mean absolute percentage error (MAPE), Root mean square error (RMSE), Relative root mean square error (RRMSE), Correlation coefficient (R), and Index of agreement (IA). The study's objective is to select the most suitable methods to evaluate the WD parameters (k and c) for calculating WDP in four meteorological stations located in Junin-Peru: Comas, Huasahuasi, Junin, and Yantac. According to the statistical index results, the ML, EMJ, and EML methods are the most accurate for each station, however, it is important to note that the methods do not perform equally well in all stations, presumably the graphical conditions and external factors play a major role.Peru is increasing, with the cumulative installed capacity expected to reach 372 MW by the end of 2018 [2]. Wind resources have a variable and fluctuating behavior [3], however, such that an exhaustive evaluation of wind speeds is necessary for effective wind-energy harnessing. A standard approach for describing velocity is via the WD, a continuous probability distribution involving the two parameters of shape and scale [3] [4] [5] [6] [7]. The WD is used across multiple research areas concerned with wind velocity, extensive literature [3]-[15] used this type of distribution.Reference [4] evaluated the effectiveness EMJ, EML, EPF, GP, ML, and Modified maximum likelihood method (MML) metrics to determine the shape and scale parameters of the WD to calculate the wind density power (WDP) at four weather stations in Canada, using three years of data. This study used the following statistical indicators: MABE, MAPE, RMSE, relative percentage error, RRMSE, and IA. The authors found that ML, EPF, EMJ, and EML are effective measurement methods. Reference [16] compared the results of five methods-GP, EMJ, ML, MML, and EPF-to determine scale and shape variables at the Babaurband wind mast in Sindh, Pakistan; the authors used R and RMSE to determine which of the WD parameter calculation methods gives the best result, and the authors found that EMJ, ML, MML, and EPF provide the optimal results when it comes to determining the scale and shape parameters of the WD. In reference [17], the authors presented four methods for estimating the Weibull parameters, namely, ML, rank regression method, mean standard deviation method, and power density method. R and RMSE were used to determine the relative precision of the parameter calculations provided by these methods. Reference [18] compared the standard deviation method and the power density method (empirical and energy pattern factor method) t...

This paper aimed to design an autonomous indirect solar dryer, which can dehydrate the aguaymanto in a costeffective manner, yielding a quality product suitable for export from the central part highland of Peru. To complete this task, it was proposed to design a prototype of autonomous solar dryer of 100 kg per batch of aguaymanto, equipped with flat reflectors and forced air feed, and powered with photovoltaic energy. This system allows to dry aguaymanto fruit at the requirements needed for its exportation. The fryer has the following dimensions: inner dimensions of the drying chamber: bottom 0.60 m, width 1.40 m, and height 1.10 m, with additional 0.05 m for insulation. Hence, the outer measures are bottom 0.70 m, width 1.50 m, and height 1.20 m. Two solar collectors are proposed with the dimensions of each: 1.50 m wide, 2.40 m long, and 0.15 m height; 2 flat mirror reflectors are required. A 80 Wp photovoltaic panel was selected for the forced air system and process control. This solar dryer is expected to cope with the problem of post-harvest deterioration. Also, it will facilitate the export by improving product quality and providing a cost-effective technology.

<p>Meteorological and electrical measurements using predictive computational techniques have been used in the analysis of photovoltaic system operation and maintenance. International standards establish general and no standardized criteria on the quality control and validation of these measurements. In the present work, a methodology has been developed to correct erroneous photovoltaic experimental measurements: radiation, temperature, current, and voltage. We validated the proposed approach with 12 case studies with more than 5,000 meteorological and electric measurements from an experimental 3 kWp photovoltaic system. The approach is based on a set of non-intrusive criteria developed from the one diode model, the approach allowed to correct about 80% of the erroneous data, 30% more using polynomial regression. As for the regression methodology, we have shown that the proposed methodology includes 4 meteorologicalelectrical variables allowing a more rigorous analysis. For 75% of the cases evaluated, the proposed methodology achieves a better data correction.</p>

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