Measured Meteorological Data

Support vector machine (SVM) based on statistical learning theory has shown its advantage in regression and prediction. This paper presents the future prediction of the solar irradiance using least square support vector machine (LSSVM) which is a kind of SVM with quadric loss function. SVM has greater generalization ability and guarantee global minima for given training data set which will give good performance for solar irradiance with time series prediction. In order to improve the prediction performance of the LSSVM, the experimental data have to be normalized and appropriate parameters are selected by generic algorithm. In this research, solar irradiance data are collected daily at monitoring station located at Green Energy Research Centre (GERC) UiTM, Shah Alam. This related information will be used in prediction of the future data which useful for designing new PV systems and monitoring existing systems performance. The results show good agreement between the predicted against the actual values measured. The proposed solar irradiance time series prediction method is considerable practical value which can be used in other datasets.

Traditionally, climate conditions have been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. First, the most appropriate inputs were selected for the models using sensitivity analysis. After that, long-term air temperatures (1 month, 1-, 2-and 3-year ahead) were modeled. Results showed that: 1) the given algorithms had acceptable results in improving the models' performance in forecasting minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R 2), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.

For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO ...

The increasing of global energy demand by 2.1% in 2017 which is more than twice the previous year's rate resulting in increasing of carbon dioxide emissions by 1.4% in the previous year after three years of remaining flat. Energy demand can be supplied by renewable energy which is more clean and help reducing carbon emissions. Solar energy has become the dominant renewable energy in Malaysia since it is situated at the equatorial region with an average solar radiation of 400-600 MJ/m2 per month. In this paper, factors that affected solar power generation are studied. All data from these factors are collected and the correlation analysis is done to determine which factor has strong correlation with solar power generation. The factors that have strong correlation with power generation will be used to predict solar power generation for next month. The results from this study showed that k-nearest neighbor method provides a better prediction result than artificial neural network since its root mean square error is the lowest value.

Solar radiation forecasting is important in solar energy power plants (SEPPs) development. The electrical energy generated from the sunlight depends on the weather and climate conditions in the area where the SEPPs are installed. The condition of solar irradiation will indirectly affect the electrical grid system into which the SEPPs are injected, i.e. the amount and direction of the power flow, voltage, frequency, and also the dynamic state of the system. Therefore, the prediction of solar radiation condition is very crucial to identify its impact into the system. There are many methods in determining the prediction of solar radiation, either by mathematical approach or by heuristic approach such as artificial intelligent method. This paper analyzes the comparison of two methods, Adaptive Neuro Fuzzy Inference (ANFIS) method, which belongs into the heuristic methods, and Multiple Linear Regression (MLP) method, which uses a mathematical approach. The performance of both methods is measured using the root mean square error (RMSE) and the mean absolute error (MAE) values. The data of the Swiss Basel city from Meteoblue are used to test the performance of the two methods being compared. The data are divided into four cases, being classified as the training data and the data used as predictions. The solar radiation prediction using the ANFIS method indicates the results which are closer to the real measurement results, being compared to the the use MLP method. The average values of RMSE and MAE achieved are 123.27 W/m 2 and 90.91 W/m 2 using the ANFIS method, being compared to 138.70 W/m 2 and 101.56 W/m 2 respectively using the MLP method. The ANFIS method gives better prediction performance of 12.51% for RMSE and 11.71% for MAE with respect to the use of the MLP method.

Traditionally, climate conditions have been one of the influential factors in population growth in worldwide. Hence, predicting these conditions can be an important step to improve life conditions in worldwide. In this study, application of genetic algorithm (GA) and particle swarm algorithm (PSO) were considered as alternatives to available algorithms for training artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict air temperature. Therefore, monthly minimum, average and maximum air temperatures of Tehran-Iran station at 64-years (1951-2014) were selected as predicted time-series. First, the most appropriate inputs were selected for the models using sensitivity analysis. After that, long-term air temperatures (1 month, 1-, 2-and 3-year ahead) were modeled. Results showed that: 1) the given algorithms had acceptable results in improving the models' performance in forecasting minimum, mean and maximum air temperatures. Also, they could improve the performance of ANN and ANFIS in most of the prediction intervals, 2) ANFIS-GA was selected as the most suitable model so that its average determination coefficient (R 2), root mean square errors (RMSE) and mean absolute errors (MAE) were 0.88, 1.41 and 2.52, respectively, 3) the sensitivity analysis provided suitable results in selecting the most appropriate model inputs for forecasting the minimum, mean and maximum air temperatures in different intervals.

For an effective and reliable solar energy production, there is need for precise solar radiation knowledge. In this study, two hybrid approaches are investigated for horizontal solar radiation prediction in Nigeria. These approaches combine an Adaptive Neuro-fuzzy Inference System (ANFIS) with Particle Swarm Optimization (PSO) and Wavelet Transform (WT) algorithms. Meteorological data comprising of monthly mean sunshine hours (SH), relative humidity (RH), minimum temperature (Tmin) and maximum temperature (Tmax) ranging from 2002-2012 were utilized for the forecasting. Based on the statistical evaluators used for performance evaluation which are the root mean square error and the coefficient of determination (RMSE and R²), the two models were found to be very worthy models for solar radiation forecasting. The statistical indicators show that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². The results show that hybridizing the ANFIS by PSO ...

ABSTRACT In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (T max), monthly mean minimum temperature (T min) and monthly mean sunshine duration ( \( \bar{n} \) ). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R 2 with the polynomial function was 0.7395 and with the radial basis function, the R 2 was 0.5877.