Meteorological Variables’ Influence on Electric Power Generation for Photovoltaic Systems Located at Different Geographical Zones in Mexico

Abstract

In this study, the relation among different meteorological variables and the electrical power from photovoltaic systems located at different selected places in Mexico were presented. The data was collected from on-site real-time measurements from Mexico City and the State of Sonora. The statistical estimation by the gradient descent method demonstrated that solar radiation, outdoor temperature, wind speed, and daylight hour influenced the electric power generation when it was compared with the real power of each photovoltaic system. According to our results, 97.63% of the estimation results matched the real data for Sonora and 99.66% the results matched for Mexico City, achieving overall errors less than 7% and 2%, respectively. The results showed an acceptable performance since a satisfactory estimation error was achieved for the estimation of photovoltaic power with a high determination coefficient R2.

An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico

“…The GDO has been used to estimate different behaviors in several studies [20,21]. As seen in [10], this method seeks the optimum β k coefficients now symbolized by θ. To achieve the minimum possible error value in Equation (4), the cost function is used.…”

“…In order to demonstrate the impact of the intelligent technique over statistical approaches, an error analysis was applied for every result obtained in Sections 3.1 and 3.2 for HS and MCS. Considering [10], Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to achieve a solid comparison between MLR, GDO and ANFIS. MAE is one of the most implemented errors in estimation studies to measure and analyze precision and is described by Equation (8), and MAPE computes the percentage error value as in Equation (9).…”

“…Since it is relevant to develop new estimation strategies in photovoltaic systems, the aim of this work is to compare ANFIS methodology with MLR and GDO as statistical approaches by comparing electrical power estimation data from photovoltaic systems [10]. It was observed that all three methods achieve a satisfactory estimation performance, but ANFIS had a better estimation capacity.…”

“…A satisfactory photovoltaic power estimation involving meteorological variables was carried out in [10] in which a Gradient Descent Optimization (GDO) minimizes the error value between the real and estimated variables after many iterations; even so, it is mentioned that other techniques may obtain better results despite their lower or greater complexity.…”

Modelo predictivo inteligente para la generación de energía eléctrica en un sistema fotovoltaico

“…Sin lugar a dudas, México tiene mucho potencial en cuanto a energía solar se refiere; si se comparara con Europa, el único país con una notoria incidencia de radiación es España alcanzando valores máximos entre 4.8 y 5.4 kWh/m 2 (ADRASE, 2018). Aun así, México excede a España alcanzando un promedio entre 5.6 y 6.2 kWh/m 2 en la región noroeste, superando incluso a China quien en su mayoría contempla valores de 4.6 kWh/m 2 (Ruz-Hernandez et al, 2019).…”