Prediction of Horizontal Daily Global Solar irradiation using artificial neural networks (ANNs) in the Castile and Leon Region, Spain

The next day's global horizontal solar irradiation is predicted using artificial neural networks (ANNs) for its application in agricultural science and technology. The time series of eight−years data is measured in an agrometeorological station, which belongs to the SIAR irrigation system (Agroclimatic Information System for Irrigation, in Spanish), located in Mansilla Mayor (León, Castile and León region, Spain). The zone has a Csb climate classification (i.e., Mediterranean Warm Summer Climate), according to Koppen−Geiger. The data for the years (2004−2010) are used for ANNs training and the 2011 as the validation year. ANN models were designed and evaluated with different numbers of inputs and neurons in the hidden layer. A neuron was used in the output layer, for all models, where the simulation of global solar irradiation for the next day on the horizontal surface results. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2 ·d), with two inputs [H(t), Kt(t)] and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2 ·d), with two inputs [H(t), J(t)] and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variable

Prediction of horizontal daily global solar irradiation using artificial neural networks (ANNs) in the Castile and León Region, Spain

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables

Predicción de la irradiación solar global diaria horizontal mediante redes neuronales artificiales en la región de Castilla y León, España

Resumen. Este artículo, se centra en la predicción de la irradiación solar global diaria horizontal, por ser el caso más interesante en la meteorología agrícola, por ejemplo, en las previsiones de necesidades de riego, utilizando la técnica de las redes neuronales artificiales (RNAs) de la inteligencia computacional, a partir de variables accesibles en las estaciones agrometeorológicas. El lugar donde fueron medidos los datos, utilizados para entrenar las RNAs, caracterizan donde se pueden volver a utilizar este tipo de modelos, en este estudio fueron las estaciones meteorológicas de la red SIAR en Castilla y León, en concreto la situada en Mansilla Mayor (León), durante los años 2004-2010. Los modelos RNAs se construyeron en la entrada con los datos medidos de irradiación solar global diaria de uno, dos y tres días anteriores, añadiendo el día del año J(t)=1..365, para predecir su valor el día siguiente. Los resultados obtenidos, validados durante el año 2011 completo RMSE=3,8012 MJ/(m2d), concluyen que las RNAs estudiadas mejoran los métodos clásicos comparados: 1) año típico CENSOLAR RMSE=5,1829 MJ/(m2d), 2) media móvil ponderada con la autocorrelación parcial de 11 días de retardo RMSE=3,9810 MJ/(m2d), 3) regresión lineal sobre el valor del día anterior RMSE=4,2434 MJ/(m2d), 4) año típico Fourier utilizado el 1er armónico RMSE=4,2747 MJ/(m2d), y 5) las matrices de transición de Markov para 33 estados posibles RMSE=4,3653 MJ/(m2d). Durante los días de cambio brusco en el nivel de irradiación solar, se observan los mayores errores de predicción. Se plantea utilizar en la entrada otras variables para mejorar la eficacia del modelo RNA. Una de las variables probadas fue el índice de claridad diario Kt=H/H0, resultando una mejora RMSE=3,7703 MJ/(m2d).Palabras clave: insolación, evapotranspiración, agrometeorología, inteligencia computacional

Estimation of the hourly global solar irradiation on the tilted and oriented plane of photovoltaic solar panels applied to greenhouse production

Agrometeorological stations have horizontal solar irradiation data available, but the design and simulation of photovoltaic (PV) systems require data about the solar panel (inclined and/or oriented). Greenhouses for agricultural production, outside the large protected production areas, are usually off-grid; thus, the solar irradiation variable on the panel plane is critical for an optimal PV design. Modeling of solar radiation components (beam, diffuse, and ground-reflected) is carried out by calculating the extraterrestrial solar radiation, solar height, angle of incidence, and diffuse solar radiation. In this study, the modeling was done using Simulink-MATLAB blocks to facilitate its application, using the day of the year, the time of day, and the hourly horizontal global solar irradiation as input variables. The rest of the parameters (i.e., inclination, orientation, solar constant, albedo, latitude, and longitude) were fixed in each block. The results obtained using anisotropic models of diffuse solar irradiation of the sky in the region of Castile and León (Spain) showed improvements over the results obtained with isotropic models. This work enables the precise estimation of solar irradiation on a solar panel flexibly, for particular places, and with the best models for each of the components of solar radiation

Synthesis of chitosan oligomers/propolis/silver-nanoparticles composite systems and study of their activity against Diplodia seriata

The synthesis and characterization of composites of oligomeric chitosan with propolis extract which allow the incorporation of a third component (silver nanoparticles) are reported, together with their application in aqueous or hydroalcoholic solutions with a view to the formation of adhesive substances or nanofilms for the protection of vineyards against harmful xylophagous fungi. The antimicrobial properties of the association of the two biological products or those resulting from the incorporation of silver nanoparticles (NPs) are studied and discussed. The efficacy of the chitosan oligomers/propolis/silver NPs ternary system is assessed in vitro for Diplodia fungi. A preliminary study on the convenience of replacing propolis with gentisic acid is also presented

Weed mapping using a machine vision system

Weed mapping is a useful tool for site-specific herbicide applications. The objectives of this study were (1) to determine the percentage of land area covered by weeds in no-till and conventionally tilled fields of common bean using digital image processing and geostatistics, and (2) to compare two types of cameras. Two digital cameras (color and infrared) and a differential GPS were affixed to a center pivot structure for image acquisition. Sample field images were acquired in a regular grid pattern, and the images were processed to estimate the percentage of weed cover. After calculating the georeferenced weed percentage values, maps were constructed using geostatistical techniques. Based on the results, color images are recommended for mapping the percentage of weed cover in no-till systems, while infrared images are recommended for weed mapping in conventional tillage systems

Life cycle assessment of a semi-indirect ceramic evaporative cooler vs. a heat pump in two climate areas of Spain

The aim of this study is to compare the environmental profile of a semi-indirect ceramic evaporative cooler (SIEC) with low environmental impact and heat pipe (HP) heat-exchanger battery with that of a Split class heat pump. The comparison is carried out for two different climate areas in Spain, one a continental or inland climate (Valladolid) and the other representative of a Western European climate (Bilbao). The environmental and economic study is conducted using life cycle assessment (LCA) with two software tools provided by SimaPro® 7.1 LCA suite (Eco-indicator[`]99® and EPS 2000®). After the LCA sensitivity analysis results it can be clearly inferred that the major contribution to the categories of damage in both facilities (SIEC and heat pump) is the class of abiotic resources, followed by human health. The high contribution to environmental impact of the evaporative condenser, part of the SIEC-HP, should also be emphasized. With regard to the heat pump, electricity proves to be the main environmental burden, followed by the pump infrastructure, in which the compressor, the external battery, the external fan and the connection have the highest impacts, respectively. The ceramic evaporative cooler SIEC-HP is both environmentally and economically more profitable than the heat pump in the region of Castilla y León (Valladolid), whereas in humid coastal areas it proves less useful due to the higher operating costs associated to this equipment. Finally, the electricity savings expressed in CO2 emissions are compared. In the inland or dry area of Spain, the ceramic evaporative cooler is the most suitable option, whilst the heat pump proves more appropriate for cities with a humid climate such as those in the Basque Country (Bilbao).Impact assessment Climatic chamber Evaporative cooler Heat pump