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

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don't satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically

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

SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP) and training procedure for neural network is done by error Back Propagation (BP). In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted

Research collaboration in solar radiometry between the University of Reunion Island and the University of Kwazulu-Natal

Paper presented to the 3rd Southern African Solar Energy Conference, South Africa, 11-13 May, 2015.Since 2012, the Universities of KwaZulu-Natal and Reunion Island have collaborated on a joint programme of solar energy research. The initiative has two principle aims: the development of solar forecasting techniques and the expansion of solar monitoring capabilities from continental Africa into the southern Indian Ocean region. In this paper, we introduce the programme and review the progress made. A key activity is performance validation of a low-cost radiometric sensor, the Delta-T Devices SPN1, which has been operated at a UKZN ground station for comparison against reference sensors. The instrument potentially represents an opportunity to expand existing radiometric networks by reducing the cost of ground station facilities. A novel feature of the device is its use of seven thermopile sensors and a stationery shading mask which together enable the simultaneous measurement of global horizontal and diffuse horizontal irradiance. It is important that the instrument performance should first be assessed, however, so that its measurement uncertainty is known ahead of deployment. Data from the UKZN trial are included in the paper, along with a description of a meteorological classification system that may be used in solar forecasting systems. The system is based on the direct solar fraction, that is, the ratio of direct horizontal irradiance to global horizontal irradiance. A clustering methodology is described and sample data are provided to illustrate the ability of the method to segregate days into statistically significant bins.cf201

Advanced Statistical Modeling, Forecasting, and Fault Detection in Renewable Energy Systems

Fault detection, control, and forecasting have a vital role in renewable energy systems (Photovoltaics (PV) and wind turbines (WTs)) to improve their productivity, ef?ciency, and safety, and to avoid expensive maintenance. For instance, the main crucial and challenging issue in solar and wind energy production is the volatility of intermittent power generation due mainly to weather conditions. This fact usually limits the integration of PV systems and WTs into the power grid. Hence, accurately forecasting power generation in PV and WTs is of great importance for daily/hourly efficient management of power grid production, delivery, and storage, as well as for decision-making on the energy market. Also, accurate and prompt fault detection and diagnosis strategies are required to improve efficiencies of renewable energy systems, avoid the high cost of maintenance, and reduce risks of fire hazards, which could affect both personnel and installed equipment. This book intends to provide the reader with advanced statistical modeling, forecasting, and fault detection techniques in renewable energy systems

Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: a review and new modeling results

We review the latest modeling techniques and propose new hybrid SAELSTM framework based on Deep Learning (DL) to construct prediction intervals for daily Global Solar Radiation (GSR) using the Manta Ray Foraging Optimization (MRFO) feature selection to select model parameters. Features are employed as potential inputs for Long Short-Term Memory and a seq2seq SAELSTM autoencoder Deep Learning (DL) system in the final GSR prediction. Six solar energy farms in Queensland, Australia are considered to evaluate the method with predictors from Global Climate Models and ground-based observation. Comparisons are carried out among DL models (i.e., Deep Neural Network) and conventional Machine Learning algorithms (i.e., Gradient Boosting Regression, Random Forest Regression, Extremely Randomized Trees, and Adaptive Boosting Regression). The hyperparameters are deduced with grid search, and simulations demonstrate that the DL hybrid SAELSTM model is accurate compared with the other models as well as the persistence methods. The SAELSTM model obtains quality solar energy prediction intervals with high coverage probability and low interval errors. The review and new modelling results utilising an autoencoder deep learning method show that our approach is acceptable to predict solar radiation, and therefore is useful in solar energy monitoring systems to capture the stochastic variations in solar power generation due to cloud cover, aerosols, ozone changes, and other atmospheric attenuation factors

Nowcasting methods for optimising building performance

In meteorology term, nowcasting is weather forecasting for the next few minutes to six hours using all immediately available weather data. It is a relatively new subject, which often involves remote sensing, numerical weather prediction models, and advanced data communication infrastructure. High-quality weather nowcasting is crucial for optimising building performance in the near future. A range of nowcasting techniques has been used for such purposes. It includes statistical, machine learning, Numerical Weather Prediction (NWP), top-down and bottom-up approaches. This paper firstly reviews the advantages and disadvantages of common nowcasting methods with the focus on solar radiation nowcasting. Based on the review, popular methods have been classified into five categories. Authors then investigated further the nowcasting data provided by weather Application Programming Interfaces (APIs) that is backed by Numerical Weather Prediction. This is due to its large-scale application potential and the significances in the most recent update on solar radiation nowcast. Secondly, the paper explores the implications of applying weather nowcasting to dynamic building simulations, most importantly, examining its impact on the accuracy of indoor temperature prediction for free float buildings, heating load prediction and heating energy for heated buildings. The study used three buildings from BESTEST ANSI/ASHRAE Standard 140-2014 as the case studies. The results show that the most recent update of weather API includes meaningful solar radiation prediction. If the building does not have a large south facing glazing, the indoor temperature and heating load predictions from dynamic models are reasonably accurate