Hourly Solar Radiation Research Articles

Performance Evaluation of Hourly Solar Radiation Models on Inclined Surfaces in Longdong Area, China

Hourly global irradiations on tilted planes are required in various engineering calculations for solar systems which must be determined by converting the solar radiation intensities measured on a horizontal surface to the incident on the tilted surface of interest. There exist a large number of models designed to perform such a conversion. This paper presents the performance of five arithmetic models used to estimate solar irradiance on inclined surfaces. Utilizing solar radiation data from June to October measured in Longdong area of Gansu province, Model performance is assessed by an inter-comparison between the calculated and measured solar global radiation on a south-oriented surface tilted at 25°. Three statistical parameters (NMBE, NRMSE and R2) have been used to confirm the calculated values. The results show that the accuracy of the models is related to the sky conditions. Liu & Jordan model shows the best calculated performance under clear sky conditions, and Klucher model perform best under overcast and cloudy conditions. The other models included Perez, Hay and Skartveit & Olseth display an intermediary performance under all sky conditions. In order to estimate the tilt angle simply, two simple approximate equations (φ-15° model and Soulayman model) based on geographical latitude and solar declination are suggested for predicting daily optimum tilt angle in summertime.

Improvement of the hourly global solar model and solar radiation for air-conditioning design in China

Hourly global solar radiation is a significant parameter for building thermal simulation and building cooling design. Most of the solar radiation models focus on the monthly or daily values, but very few studies have been carried out on the hourly values. The Zhang and Huang model was developed for estimating the hourly solar radiation based on the dry-bulb temperature difference, relative humidity and cloud cover. In this study, a new model is proposed for all-sky conditions based on the hourly/daily radiation ratio retrieved from the Zhang and Huang model and the measured daily solar radiation dataset. Compared with the Zhang and Huang model, the proposed model is more accurate (the average of R2, 0.90) and the average of RMSE is improved by 29.54%. The latest hourly solar radiation dataset for 17 locations in China from 2006 to 2016 is subsequently established using the proposed model, which can be used to generate the latest Typical Meteorological Year (TMY). As one of the significant applications to buildings, two kinds of hourly solar radiation datasets (the frequency levels of 95% and 97.5%) for 17 locations are also developed, which are usable for air-conditioning design.

Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea

Forecasting solar radiation has recently become the focus of numerous researchers due to the growing interest in green energy. This study aims to develop a seasonal auto-regressive integrated moving average (SARIMA) model to predict the daily and monthly solar radiation in Seoul, South Korea based on the hourly solar radiation data obtained from the Korean Meteorological Administration over 37 years (1981–2017). The goodness of fit of the model was tested against standardized residuals, the autocorrelation function, and the partial autocorrelation function for residuals. Then, model performance was compared with Monte Carlo simulations by using root mean square errors and coefficient of determination (R2) for evaluation. In addition, forecasting was conducted by using the best models with historical data on average monthly and daily solar radiation. The contributions of this study can be summarized as follows: (i) a time series SARIMA model is implemented to forecast the daily and monthly solar radiation of Seoul, South Korea in consideration of the accuracy, suitability, adequacy, and timeliness of the collected data; (ii) the reliability, accuracy, suitability, and performance of the model are investigated relative to those of established tests, standardized residual, autocorrelation function (ACF), and partial autocorrelation function (PACF), and the results are compared with those forecasted by the Monte Carlo method; and (iii) the trend of monthly solar radiation in Seoul for the coming years is analyzed and compared on the basis of the solar radiation data obtained from KMS over 37 years. The results indicate that (1,1,2) the ARIMA model can be used to represent daily solar radiation, while the seasonal ARIMA (4,1,1) of 12 lags for both auto-regressive and moving average parts can be used to represent monthly solar radiation. According to the findings, the expected average monthly solar radiation ranges from 176 to 377 Wh/m2.

An Advanced Simple Method for Generating Synthetic Average Instant Hourly Solar Energy

The main objective of this study is to generate accurate synthetic hourly solar radiation data by using an easily accessible open source data. In this regard, a new approach is proposed for estimation of synthetic hourly global solar radiation during the day by utilizing only annual solar energy data. First time in literature, a model has been developed for prediction hourly and daily solar radiation based on annual solar energy parameter in this study. Parameters of the model were generated and tested for Turkey and one of them was presented as a case study within this paper. Long term measured hourly horizontal solar irradiance data from a network of Turkish meteorological stations was used to calibrate the model function. The predictions are compared with the solar data available in literature for Turkey. The advanced simple new model is utilized in open source computer program and has the potential to be adapted to other countries.

Forecasting Hourly Solar Irradiance Using Hybrid Wavelet Transformation and Elman Model in Smart Grid

With the integration of photovoltaic (PV) power into an electrical network, the complexity of the grid management is increasing because of intermittent and fluctuation nature of solar energy. Solar irradiance forecasting is essential to facilitate planning and managing electricity generation and distribution in smart grid cyber-physical system (CPS). The performance of existing short-term forecasting methods is far from satisfactory due to a lack of reliable and fast time-frequency model for continuous-time solar irradiance data. To address this problem, this paper proposes a new method, Elman Neural Network (ENN) driven Wavelet Transform (WT-ENN), for hourly solar irradiance forecasting. Firstly, the solar irradiance series was decomposed into a set of constitutive series using wavelet transform. Secondly, the new wavelet coefficients were predicted by ENNs in every sub-series with the best network structure and parameters. Thirdly, Wavelet reconstruction will predict next hour solar irradiance through the aggregation of outputs of the ensemble of ENNs. Finally, the forecasting performance was evaluated using two large real-world solar irradiance datasets. Experiment results show that the new WT–ENN model outperforms a large number of alternative methods and an average forecast skill of 0.7590 over the persistence model. Thus, it is concluded that the proposed approach can significantly improve the forecasting accuracy and reliability.

A stochastic model for the hourly solar radiation process for application in renewable resources management

Abstract. Since the beginning of the 21st century, the scientific community has made huge leaps to exploit renewable energy sources, with solar radiation being one of the most important. However, the variability of solar radiation has a significant impact on solar energy conversion systems, such as in photovoltaic systems, characterized by a fast and non-linear response to incident solar radiation. The performance prediction of these systems is typically based on hourly or daily data because those are usually available at these time scales. The aim of this work is to investigate the stochastic nature and time evolution of the solar radiation process for daily and hourly scale, with the ultimate goal of creating a new cyclostationary stochastic model capable of reproducing the dependence structure and the marginal distribution of hourly solar radiation via the clearness index KT.

Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting

Due to the chaotic nature of the underlying physical processes, even state-of-the-art models cannot perfectly forecast the solar irradiance at the surface of the earth. There is, therefore, a growing interest in the research community for forecasting methods that can quantify their own uncertainty.This paper proposes a novel probabilistic framework for forecasting day-ahead hourly solar irradiance. A principal component analysis (PCA) is used to tightly combine a high-resolution mesoscale numerical weather prediction (NWP) model with a quantile gradient boosting algorithm.A thorough evaluation of the deterministic and probabilistic properties of the model is conducted for a full year in the tropical island of Singapore. The impact of the sky conditions on its performance is also considered. Furthermore, a rigorous statistical framework is employed to systematically benchmark our model against two state of the art methods, a Lasso model output statistic procedure and an analog ensemble (AnEn). Our model significantly improves the numerical weather prediction model: it achieves a 41% reduction of the MAE and 39% reduction of the RMSE. It is also slightly more accurate than Lasso and has a CRPS 4% lower than that of AnEn.

A novel time-effective model for daily distributed solar radiation estimates across variable terrain

Accurate and precisess estimation of spatio-temporal variability of solar radiation is critical. Some commonly used models evaluate this variability using methods in which the data required for estimating atmospheric attenuation may not be easily accessible for some study areas. Here, a daily solar radiation estimation method which uses ambient air temperature, a Digital Elevation Model, time of year, and monthly radiation estimates from Solar Analyst model has been proposed. The objective was to use air temperature-based empirical models for atmospheric transmissivity and diffuse fractions to vary total monthly radiation estimation from Solar Analyst, and then calculate total daily radiation as a fraction of total monthly radiation by applying a daily transmissivity-based ratio, as air temperature data are readily available at most locations on the planet. Results revealed that daily solar radiation can be estimated very well, with Mean Absolute Bias Error of around 40–53 W m−2 or Mean Bias Error of ± 10%, under all sky conditions at seven sites in diverse climate regions, using significantly less input data. The presented method is an improvement over previously used methods with Mean Bias Error of under 10% but more input parameters. Furthermore, the hourly solar radiation values can be calculated using the presented method using the ratio between daily and hourly radiation, for example from literature values and estimated daily insolation. The result also showed that the method is more useful for those stations with substantially higher numbers of sunny days than cloudy or partly cloudy days because the uncertainty of the model decreased from cloudy to sunny sky conditions. The implemented Digital Elevation Models environment of this method makes it applicable in many studies that need spatial estimation of solar radiation, especially for solar energy generation projects.

Evaluation of a cloud cover based model for estimation of hourly global solar radiation in Western Canada

ABSTRACTCloud cover based solar radiation models are relatively simple and convenient as the models require the input of cloud cover data which are mostly available from the meteorological stations. In this study, the performance of a cloud cover based solar radiation model (Kasten–Czeplak model) with original or locally fitted coefficients was evaluated for estimating the hourly global solar radiation for four different locations in Western Canada. The average value of R2, mean bias error, and root mean square error are 0.69, −61.6, and 157.9 W m−2, respectively, for the model with original coefficients, whereas 0.82, 4.4, 107.1 W m−2 with locally fitted coefficients. Results show that the Kasten–Czeplak model with locally fitted coefficients satisfactorily estimated the hourly solar radiation of four different locations in Western Canada. Also, the results indicate that the model with original coefficients has very limited accuracy under intermediate cloud cover conditions.

Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it

Hourly solar radiation in Depok, West Java, Indonesia (106.7942 Longitude, -6.4025 Latitude)

The objective of this paper is to predict hourly solar radiation in Faculty of Engineering, Universitas Indonesia, Depok, West Java, Indonesia (106.7942 longitude, -6.4025 latitude) as studied site. The calculation is based on several empirical models and compared to the measured data from BMKG. The comparison of theoretical data and measured data in order to know the influence of solar radiation parameters like solar irradiance condition, angstrom turbidity coefficient, and climate condition used to calculate the solar radiation at studied site. In order to evaluate the performance of solar radiation models, a statistical test was performed by using Mean Bias Error and Root Mean Square Error. The results indicate the best performance of solar radiation models that applicable in Indonesia. Therefore, Benjamin et al. model provides good performances for estimation of the hourly solar radiation.

Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms

Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This study proposes machine learning algorithms to predict the hourly solar irradiance. Forecasting models were developed based two types of the input data. The first one uses local time, temperature, pressure, wind speed, and relative humidity as input variables of the models (N1); the second one is the time-series prediction of solar irradiance (N2) (forecasting models only use from past time-series solar radiation values to estimate the future values). For this purpose, multilayer feed-forward neural network (MLFFNN), radial basis function neural network (RBFNN), support vector regression (SVR), fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are developed. The results demonstrated that for the N1, SVR and MLFFNN models have the maximum performance to predict the solar irradiance with R = 0.9999 and 0.9795, respectively. For the N2, SVR, MLFFNN and ANFIS models have reported the correlation coefficient more than 0.95 for the testing dataset.

A direct steam generation concentrated solar power plant with a decalin/naphthalene thermochemical storage system

This study presents the design and analysis of a new integrated direct steam generation (DSG) concentrated solar power (CSP) plant with a decalin/naphthalene thermochemical storage system. Model simulations were performed in accordance with historical hourly solar radiation data over a year, using a combination of Aspen Plus v10, MATLAB 2016b, and Microsoft Excel VBA. It was found that the proposed plant feasibly stored and discharged energy, based on the solar radiation and chemical storage availability, to maintain base-load power productions (250MWe or 120MWe) with an overall efficiency of 14.6%. The effectiveness of the designed storage system was found to be comparable to a molten salt storage system which is currently used in existing CSP plants. The proposed integrated DSG CSP plant with a decalin/naphthalene thermochemical storage system shows promise for being an alternative to existing CSP plants.

Concentrating or non-concentrating solar collectors for solar Aided Power Generation?

The preheating of the feedwater in a Regenerative Rankine Cycle power plant with solar thermal energy, termed Solar Aided Power Generation, is an efficient method to use low to medium temperature solar thermal energy. Here, we compared the use of medium temperature (200–300°C) energy from concentrating solar collectors (e.g. parabolic trough collectors) to displace the extraction steam to high temperature/pressure feedwater heaters with that from low temperature (100–200°C) non-concentrating solar collectors (e.g. evacuated tube collectors) to displace the extraction steam to low temperature/pressure feedwater heaters of the power plant. In this paper, the in terms of net land based solar to power efficiency and annual solar power output per collector capital cost of a Solar Aided Power Generation using concentrating and non-concentrating solar collectors has been comparted using the annual hourly solar radiation data in three locations (Singapore; Multan, Pakistan and St. Petersburg, Russia). It was found that such a power system using non-concentrating solar collectors is superior to concentrating collectors in terms of net land based solar to power efficiency. In some low latitude locations e.g. Singapore, using non-concentrating solar collectors even have advantages of lower solar power output per collector capital cost over using the concentrating solar collectors in an SAPG plant.

Microclimatic temperatures increase the potential for vector-borne disease transmission in the Scandinavian climate

We quantified the difference between the meteorological temperature recorded by the Danish Meteorological Institute (DMI) weather stations and the actual microclimatic temperatures at two or three different heights at six potential insect habitats. We then compared the impact of the hourly temperature on the extrinsic incubation period (EIP) of six pathogens. Finally, we developed a regression model, enabling us to predict the microclimatic temperatures of different habitats based on five standard meteorological parameters readily available from any meteorological institution. Microclimatic habitats were on average 3.5–5 °C warmer than the DMI recorded temperatures during midday and 1–3 °C cooler at midnight. The estimated EIP for five of the six microclimatic habitats was shorter than the estimates based on DMI temperatures for all pathogens studied. The microclimatic temperatures also predicted a longer season for virus development compared to DMI temperatures. Based on DMI data of hourly temperature, solar radiation, wind speed, rain and humidity, we were able to predict the microclimatic temperature of different habitats with an R2 of 0.87–0.96. Using only meteorological temperatures for vector-borne disease transmission models may substantially underestimate both the daily potential for virus development and the duration of the potential transmission season.

Improving Solar Power PV Plants Using Multivariate Design Optimization

The proliferation of photovoltaic (PV) installations across the globe has accelerated dramatically in the past decade covering home, rural, mobile, industrial, and utility-scale applications. In all these cases, improving payback time and energy production for PV installations is a very complex design tradeoff that involves multiple variables such as irradiance fluctuations, inverter efficiency, operating temperature variation, and PV panel type. In this paper, a detailed multivariate study of PV plant design is presented, resulting in an improved technique to increase the potential benefits of solar plants with lower capital costs. This new approach includes detailed consideration of the probabilistic hourly temperature and solar irradiation profile of the installation site, the efficiencies and operating areas of different grid-tie inverters, and detailed models of different PV modules in the optimal design process. The harvested energy, total costs, and payback time are the objective functions in this approach, while the number of series and parallel panels, the tilt angle, and inverter topology and PV module type are determined from a list of possible candidates. The optimization process is implemented for a sample system, and the results are compared to both a traditional and design software approach. It is seen that by applying the proposed approach with lower capital costs, the harvested energy, financial benefits, and the payback time can be improved by 9.3%, 1%, and 6.95%, respectively. Several case studies are then presented to investigate the sensitivity and robustness of the design with regard to the ambient temperature variation, solar irradiation fluctuation, and available surface area for PV module installation.

Time series analysis and short-term forecasting of solar irradiation, a new hybrid approach

Abstract In this paper, nonlinear time series analysis and short-term prediction of solar irradiation were considered, simultaneously. The proposed methodology is to employ time series analysis methods as well as swarm and evolutionary algorithms in conjunction with well-known regression, fuzzy and neural network model structures to develop a simple but efficient and applicable model for solar irradiation forecasting. The employed experimental data was the hourly solar irradiation of Qazvin city in Iran for five years. At first, the solar irradiation data was normalized using the daily clear sky irradiation data which is an annually periodic time series. Then, the properties of normalized solar irradiation were characterized via time series analysis methods such as recurrence plots, autocorrelation and mutual information analysis. Based on these analyses, each year was divided into two seasons, the sunny and cloudy seasons which are noticeably different in dynamics. Next, a hybrid but simple model was developed to predict the solar irradiation in different seasons. For the sunny season, an optimized multivariate regression model was proposed; and for the cloudy season a bi-level model consisting of an optimized regression model and ANFIS was developed. The model parameters were tuned optimally by various evolutionary algorithms being GA, PSO, ABC, COA, and flower pollination algorithm (FPA). A Fourier-type model was also developed for modeling of the clear sky data. The results showed the out-performance of FPA method in tuning of the model parameters and convergence time. Besides, the performance of the proposed bi-level model was evaluated in comparison with some other model structures such as artificial neural networks, ANFIS networks, LSE-regression models, LS-support vector machines model, etc. The results showed that the proposed method performs considerably better than the other methods in forecasting the solar irradiation time series in both sunny and cloudy seasons.

A Probability Model of PV for the Middle-term to Long-term Power System Analysis and Its Application

Abstract This paper proposes a probability model for the Photovoltaic (PV) power output which is adaptive for the middle-term to long-term power system analysis. In the first step, a discrete random time series model for the mean value of hourly solar irradiance is presented according to meteorology models. Then a Beta distribution model works to describe the stochastic vibration of the hourly solar irradiance around its mean value. The proposed model can describe the long term behavior of PV generator and has the advantages of less parameters and lower requirements on PV history data. It can also effectively reflect the correlation among PV generators and loads related to locations and life styles. An example of PV location and capacity selection on a distribution feeder is presented to illustrate the application and the feasibility of the model. The proposed model is applicable in power system planning, reliability analysis and many other power system long-term analyses with PV sources.

Correlation to Predict Global Solar Insolation and Evaluation of that Correlation for Korea(Ⅰ)

Recently, several studies have been conducted to show how the horizontal global solar radiation in Korea can be predicted using the Zhang-Huang (ZH) model proposed in 2002 for China. Previous results showed that that Cloud-cover Radiation Model (CRM) is unsuitable for Korea. Therefore, this study first corrected the coefficients of the ZH model to make it applicable to Korea and showed that ZH fitted model could make a prediction. This work added a term with the duration of sunshine as being closely related to the hourly solar radiation in Korea into the ZH model. Finally, this study proposed a new model to predict the hourly and daily total solar radiation for all regions, various seasons, and various weather conditions, including overcast and clear, with higher accuracy and lower error than other models proposed in Korea.

Generation of typical meteorological years for the Argentine Littoral Region

This work describes the generation of the typical meteorological year (TMY) for 15 locations all around the Littoral Region in northeastern Argentina, southeastern South America. The originally available weather data at each location contain, among others, dry-bulb and dew-point temperatures, wind velocity, and total sky cover, hourly measured during the period 1994–2014 by the National Meteorological Service (SMN) of Argentina. From other sources, two of these locations have hourly measured solar radiation during a few years. These radiation measurements were used to calibrate an existing Zhang–Huang solar radiation model that was then used to calculate the hourly solar radiation for the entire weather data base.Once we complete the long-term weather database at a given location, we define the typical meteorological year (TMY) at this location as the concatenation of 12 typical meteorological months (TMM). The typicality of a month is measured using Finkelstein–Schafer statistics based on nine daily indices (maximum, minimum and mean dry-bulb and dew-point temperatures, maximum and mean wind velocity, and global solar radiation).We finally show an example of application of the current TMYs for building energy simulation in a location deep inside Littoral. Subsequently we show the importance of the newly developed local TMY above using original TMY from neighbouring locations.