Machine learning methods for solar radiation forecasting: A review

Several simplified design procedures for solar energy systems require monthly average meteorological data. Monthly average daily totals of the solar radiation incident on a horizontal surface are available. However, radiation data on tilted surfaces, required by the design procedures, are generally not available. A simple method of estimating the average daily radiation for each calendar month on surfaces facing directly towards the equator has been presented by Liu and Jordan [1]. This method is verified with experimental measurements and extended to allow calculation of monthly average radiation on surfaces of a wide range of orientations.

Calculation of monthly average insolation on tilted surfaces

Matlab (the language of technical computing): using matlab graphics ver.5

A time-interleaved multichannel analog-to-digital converter (ADC) achieves high sampling rates with the drawback of additional distortions caused by channel mismatches. In this paper, we consider the dependency of the signal-to-noise-and-distortion ratio (SINAD) on the combination of several different channel mismatch effects. By using either explicitly given mismatch parameters or given parameter distributions, we derive closed-form equations for calculating the explicit or the expected SINAD for an arbitrary number of channels. Furthermore, we extend the explicit SINAD by the impact of timing jitter. We clarify how channel mismatches interact and perform a worst case analysis of the explicit SINAD for individual mismatch errors. We also show that equations describing the expected SINAD of individual mismatch errors are special cases of our general formulation. We indicate how to use the expected SINAD for finding efficient optimization priorities and demonstrate the importance of worst case analyses.

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The impact of combined channel mismatch effects in time-interleaved ADCs

Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms

Information about the availability of solar radiation on horizontal surface is essential for the optimum design and study of solar energy systems. For a country like UAE, the efficient application of solar energy seems inevitable because of abundant sunshine available throughout the year. The traditional way of knowing the amount of global solar radiation (GSR) in a particular region is to install pyranometers at as many locations as possible in this region thus requiring daily maintenance and data recording, and consequently increasing cost of GSR data collection. Therefore, it is rather more economical to develop methods to estimate the GSR using climatological parameters (Akhlaque et al., 2009; Kassem et al., 2009; Falayi et al., 2008; El-Sebaii & Trabea, 2005). Many researchers estimated global solar radiation by using artificial neural networks. (Mohandes et al, 1998) applied ANN techniques to predict GSR using weather data from 41 stations in Saudi Arabia. Data from 31 stations was used in training the NN and the remaining data was used for testing. Input variables to the NN included 4 parameters: latitude, longitude, altitude and sunshine duration. Their sample data was not large enough to allow a credible comparison between the ANN models used and empirical regression models. (Lam et al., 2008) have used Artificial Neural networks ANNs to develop predication models for daily global solar radiation using measured sunshine duration for 40 cities covering 9 major thermal climatic zones and sub-zones in China. (Alam et al., 2009) used ANNs to estimate monthly mean hourly and daily diffuse solar radiation based on weather data from 10 Indian stations which have different climatic conditions. (Alawi & Hinai, 1998) applied ANNs to predict solar radiation in areas not covered by direct measurement instrumentation. The input data that was used for building the network were the location, month, mean pressure, mean temperature, mean vapor pressure, mean relative humidity, mean wind speed and mean duration of sunshine. (Tasadduq et al., 2002) have used neural networks for the prediction of hourly mean values of ambient temperature 24 hours in advance. Full year hourly values of ambient temperature are used to train a neural network model for a coastal location — Jeddah, Saudi Arabia. (Elminir et al., 2005) applied ANN modeling techniques to predict solar radiation data in different spectrum bands from data of meteorology for Helwan (Egypt) meteorology monitoring station. (Rehman & Mohandes, 2008) developed ANN-based estimation GSR for Abha city in Saudi Arabia; by

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Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE

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Engineering Education and Research Using MATLAB

The geographical location (latitude: 24° 16′ N and longitude: 55° 36′ E) of Al Ain city in the southwest of United Arab Emirates (UAE) favors the development and utilization of solar energy. This paper presents an artificial neural network (ANN) approach for predicting monthly global solar radiation (MGSR) on a horizontal surface in Al Ain. The ANN models are presented and implemented on 13-year measured meteorological data for Al Ain such as maximum temperature, mean wind speed, sunshine, and mean relative humidity between 1995 and 2007. The meteorological data between 1995 and 2004 are used for training the ANN and data between 2004 and 2007 are used for testing the predicted values. Multilayer perceptron (MLP) and radial basis function (RBF) neural networks are used for the modeling. Models for the MGSR were obtained using eleven combinations of data sets based on the above mentioned measured data for Al Ain city. Forecasting performance parameters such as root mean square error (RMSE), mean bias error...

Ali Assi

Papers

Using MATLAB to Develop Artificial Neural Network Models for Predicting Global Solar Radiation in Al Ain City – UAE

Engineering Education and Research Using MATLAB

Artificial neural networks for predicting global solar radiation in al ain city - uae

Enhancing the performance of heaving wave energy converters using model-free control approach

Effect of dust cleaning on the enhancement of the power generation of a coastal PV-power plant at Zahrani Lebanon