Hybrid models for global solar radiation prediction: a case study

ABSTRACT

This paper presents a comparison between different prediction models for solar radiation application. The present study assessed the performance of multi-layer perceptron (MLP) as well as boosted decision tree, and used a new combinition of these models with linear regression for the prediction of daily global solar irradiation (DGSR). The performance of the studied models was validated using a real dataset measured at the Applied Research Unit for Renewable Energies (URAER) situated in the south of Algeria. Different input combinations have been analysed in order to select the relevant input parameters for DGSR prediction. The results acheived show that the MLP model perfoms better than the others models in terms of statistical indicators: normalised root mean square error (0.033) and R² (97.7%).     

Performance assessment of empirical models for prediction of daily and monthly average global solar radiation: the case study of Ibadan,Nigeria

In this study, the performance assessment of empirical models for modelling global solar radiation in Ibadan is presented. The empirical models are derived from the three basic models: Angstrom–Prescott model, Garcia model and Hargreaves–Sammani model. The data used in the analysis consist of daily average global solar radiation, daily average sunshine hours, daily average maximum temperature and daily average minimum temperature collected over a period of nine years (2000–2008). Regression constants are determined for each of the model for each months of the year. The study reveals that Garcia Quadratic model puts up the best overall performance. The model can predict the daily average global solar irradiation with Mean Absolute Error of 1.86?MJ?m^?2?day^?1, Root Mean Square Error of 2.7?MJ?m^?2?day^?1, Mean Absolute Percentage Error 9.34% and Coefficient of Determination (R²) of 0.68.     

Multi-step-ahead forecasting of daily solar radiation components in the Saharan climate

ABSTRACT

Accurate estimation of renewable energy sources plays an important role in their integration into the grid. An unexpected atmospheric change can produce a range of problems related to various solar plant components affecting the electricity generation system. Global solar radiation (GSR) assessment has been increased in the past decade due to its important use in photovoltaic application. In this paper, we propose the use of machine learning-based models for daily global and direct solar radiation forecasting in a semi-arid climate, using a combination set of meteorological parameters on a horizontal surface in the Ghardaïa region. The models are presented and implemented on 3-year measured meteorological data at Applied Research Unit for Renewable Energies (URAER) at Ghardaïa city between 2014 and 2016. The results show that both MLP and RBF models perform well for three-step-ahead forecasting with a slight improvement in MLP models in terms of statistical metrics.     

An Analytical Study on global radiation and meteorological parameters for India

ABSTRACT

A study is carried out for global radiation (global horizontal and global tilted radiation) and meteorological parameters (humidity and temperature) recorded for a period of one year (2011) at the National Institute of Solar Energy (NISE), Gwal Pahri (28.42°N, 77.15°E), India. Maximum global horizontal radiation of 7.22?kWh/m² is recorded in the month of June while minimum of 0.91?kWh/m² is observed in February. The highest value of the tilted radiation 7.27?kWh/m² is recorded in March and the lowest value 0.90?kWh/m² is noticed in February. The maximum temperature of 36.5°C and humidity of 87.6% are observed in the months of June and July, respectively. Conversely, minimum temperature of 6.1°C and humidity 21.7% are noticed in the months of January and April, respectively. Furthermore, meteorological parameters have been correlated with global radiation on horizontal and tilted surface. The study is vital for the performance analysis of different solar energy applications.     

Soft computing for solar radiation potential assessment in Algeria

ABSTRACT

Precise estimation of solar radiation is a highly required parameter for the design and assessment of solar energy applications. Over the past years, many machine learning techniques have been proposed in order to improve the forecasting performance using different input attributes. The aim of this study is the forecasting of one day ahead of horizontal global solar radiation using a set of meteorological and geographical inputs. In this respect, the Gaussian process regression methodology (GPR) and least-square support vector machine (LS-SVM) with different kernels are evaluated in order to select the most appropriate forecasting model. In order to assess the proposed models, the southern Algerian city, Ghardaia regions, was selected for this study. A historical data of five years (2013–2017) of meteorological data collected at Renewable Energies (URAER) in Ghardaia city are used. The achieved results demonstrate that all the proposed models give approximately similar results in terms of statistical indicators. In term of processing time, all the models showed acceptable computational efficiency with less computational costs of the GPR model among all machine learning models.     

Estimation of horizontal diffuse solar radiation from common meteorological parameters: a case study for Jeddah,Saudi Arabia

The measured data of global and diffuse solar radiation on a horizontal surface, number of sunshine hours, mean daily ambient temperature, maximum and minimum ambient temperatures, relative humidity and amount of cloud cover, for Jeddah (latitude 21° 42′ 37″ N, longitude 39° 11′ 12″ E), Saudi Arabia for the period (1996–2006) are analysed. The data are employed to develop empirical correlations between the monthly average daily diffuse fraction (H _d /H) or diffuse transmittance (H _d /H ₀) and various meteorological parameters such as relative number of sunshine hours (s/s ₀), ambient temperature T, relative humidity R _h and amount of cloud cover c _w. The derived correlations are evaluated by making comparisons between measured and calculated values of monthly average daily diffuse radiation H _d. All the proposed correlations are found to be able to predict the monthly and annual averages daily diffuse radiation with excellent accuracy. It is also inferred that, if the data of the monthly average daily global radiation H and number of bright sunshine hours s are not available, the values of H _d for the hot-humid zone of Saudi Arabia can be estimated with a reasonable accuracy from the correlations between H _d /H ₀ and the mean daily ambient temperature T.     

Modelling of synthesis of waste cooking oil methyl esters by artificial neural network and response surface methodology

This present study was carried out to investigate the application of artificial neural network (ANN) and response surface methodology (RSM) as modelling tools for predicting the waste cooking oil methyl esters (WCOME) yield obtained from alkali-catalysed methanolysis of waste cooking oil (WCO). The impact of process parameters involved was studied by a central composite rotatable design. A comparison of the two developed models for the methanolysis process was carried out based on pertinent statistical parameters. The calculated values of coefficient of determination (R²) of 0.9950 and the average absolute deviation (AAD) of 0.4930 for the ANN model compared with R² of 0.9843 and AAD of 0.9376 for the RSM model demonstrated that the ANN model was more accurate than the RSM model. The actual maximum WCOME yield of 94?wt% was obtained at a reaction temperature of 55°C, a catalyst amount of 1?w/v, a reaction time of 70 min and a methanol-to-oil ratio of 6:1.

Abbreviations/Nomenclature CV: coefficient of variance; FFA: free-fatty acid; R: correlation coefficient; R²: coefficient of determination     

Controlling power output of solar chimney power plant according to demand

ABSTRACT

The solar chimney power plant (SCPP) is a simple solar thermal power plant that is capable of converting solar energy into thermal energy in the solar collector. In the second stage, the generated thermal energy is converted into kinetic energy in the chimney and ultimately into electric energy using a combination of a wind turbine and a generator. The numerical simulations were performed for the geometry of the prototype in Manzanares, Spain. Using computational ?uid dynamics (CFD) techniques; we have simulated a two-dimensional axisymmetric model of a SCPP with the RNG k-ε turbulence. In this model, the discrete ordinates (DO) radiation model was implemented to solve the radiative transfer equation, using a two-band radiation model. The main objective of this work is to explore dynamic control over plant power output. We have presented a technique to control the power output of the solar chimney power plant, in order to deliver power according to specified demand patterns. In order to present this, the reference plant model was modified to include a secondary and tertiary collector roof under the existing main collector. In terms of base load electricity generation, the inclusion of a secondary and tertiary collector roof produces good control over plant output.     

Energy partition and conversion of solar and thermal radiation into sensible and latent heat in a greenhouse under arid conditions

For a greenhouse thermal analysis, it is essential to know the energy partition and the amount of solar and thermal radiation converted into sensible and latent heat in the greenhouse. Factors that are frequently needed are: efficiency of utilization of incident solar radiation (π), and sensible and latent heat factors (η and δ). Previous studies considered these factors as constant parameters. However, they depend on the environmental conditions inside and outside the greenhouse, plants and soil characteristics, and structure, orientation and location of the greenhouse. Moreover, these factors have not yet been evaluated under the arid climatic conditions of the Arabian Peninsula.In this study, simple energy balance equations were applied to investigate π, η and δ; energy partitioning among the greenhouse components; and conversion of solar and thermal radiation into sensible and latent heat. For this study, we used an evaporatively cooled, planted greenhouse with a floor area of 48 m². The parameters required for the analysis were measured on a sunny, hot summer day. The results showed that value of π was almost constant (≅0.75); whereas the values of η and δ strongly depended on the net radiation over the canopy (R_na); and could be represented by exponential decay functions of R_na.At a plant density corresponding to a leaf area index (LAI) of 3 and an integrated incident solar energy of 27.7 MJ m⁻² d⁻¹, the solar and thermal radiation utilized by the greenhouse components were 20.7 MJ m⁻² d⁻¹ and 3.74 MJ m⁻² d⁻¹, respectively. About 71% of the utilized radiation was converted to sensible heat and 29% was converted to latent heat absorbed by the inside air. Contributions of the floor, cover and plant surfaces on the sensible heat of the inside air were 38.6%, 48.2% and 13.2%, respectively.     

Heat transfer modelling on windows and glazing under the exposure of solar radiation

Due to the effect of solar radiation on windows and glazing system the evaluation of heat flow is of primary importance in modeling the thermal performance within building interiors to account thermal comfort and overall energy consumption of a building. In this context the optical properties of window glazing are measured to determine the percentage absorption of incident solar radiation. An experimental study was performed in a room to measure the glazing surface temperature due to the global radiation on it. The corresponding window plane global radiation and horizontal global radiation were measured outside for simulation. Mathematical models have been developed to simulate the window plane solar radiation and corresponding glazing surface temperature aiming at validating the measured values. The thermal model is concerned with laminar heat transfer for natural and forced convection process according to the ambient conditions. The estimated errors between experimental and simulated values of window plane radiation and glazing temperature are shown to be within ±5%. Using the developed thermal model the heat flow inside the room through windows is determined. Thus overall heat transfer coefficient of glazing (U-factor) and the Solar Heat Gain (SHG) of building interior have been predicted from the simulation.     

Parameter extraction of solar photovoltaic cells and modules using current–voltage characteristics

ABSTRACT

The significance of bio-inspired evolutionary algorithms has attracted many applications for obtaining best solutions to their optimisation problems in the past decades. This paper is about the application of one of these algorithms, namely, quantum particle swarm optimisation algorithm for parameter extraction of solar photovoltaic cells using current–voltage (I–V) characteristics. This algorithm has been used here to extract five parameters, namely, photocurrent, saturation current, series resistance, shunt resistance and ideality factor that influence the I–V relationship of single diode model solar photovoltaic cells. This approach has been validated for a cell and a module. Simulations using Matlab software have shown that the simulated I–V characteristics obtained using the extracted parameters have good agreement with the experimental I–V values. The reason for the interest taken in undertaking this work is to suggest a good and an accurate simulator for solar system designers.     

A method for modelling photovoltaic modules under non-standard conditions of solar radiation and ambient temperature in Quibdó, Colombia

This work describes a mathematical model developed to estimate the I–V and P–V curves of photovoltaic modules operating under non-standard conditions of irradiance and temperature in Quibdó, Colombia.

The model was implemented using the Matlab? software using an equivalent circuit of a diode for a photovoltaic panel. The input parameters of solar radiation and ambient temperature were obtained from a monitoring station installed in the city of Quibdó. The average annual solar radiation was 256.96?W/m² while the annual average temperature was 27.59°C in 2015. The model resulted in curves for I–V and P–V. These were compared with simulated results from the information reported by the manufacturer of a polycrystalline 250?W silicon solar panel (reference AS-6P30), which is part of a photovoltaic system 2?kW interconnected with the grid. This resulted in an average error of 0.5% for the I–V curve and 0.3% for the P–V curve.     

Experimental investigation on degradation of heat transfer properties of a black chromium-coated aluminium surface solar collector tube

ABSTRACT

In our earlier research, we have studied the surface radiation property degradation of the solar collector receiver tube material [Logesh, K., R. Ganesh, I. Saran Raj, V. Ramesh, and B. Tharun Raj. 2017. “Experimental Investigation on Radiation Heat Transfer Properties Degradation of Aluminium Solar Receiver Tube Material.” International Journal of Ambient Energy. doi:10.1080/01430750.2017.1335230]. In this study, the examination is expanded to degradation property of the selective surface, black chromium-coated aluminium material. Black chrome is mainly used for its high corrosion resistance. The selective surface has been examined for its emissivity (ε) and absorptivity (α) changes due to its sunlight exposure at elevated temperatures. The exposure duration ranges from 240 to 960?h. In the selected range of exposure, the properties are measured in four intervals. The emissivity (ε) is found to decrease, whereas the absorptivity (α) gets increased for coated than the uncoated surfaces for the same duration of exposure. The examination is also carried out to find the nature of the heat transfer properties determined by its temperature. For every duration of exposure, the properties are measured at 10 temperature intervals.     

Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor

In the present paper, artificial neural network (ANN) modelling has been performed for evaluating power coefficient (C_p) and torque coefficient (C_t) of a combined three-bucket-Savonius and three-bladed-Darrieus vertical axis wind turbine rotor, which has got potential for power generation in a small-scale manner, especially in low wind speed conditions. However, detailed experimental work on the rotor for evaluating its performance parameters is either scarce or too costly and time consuming to carry out. In this work, a new ANN modelling method is adopted to map the input–output parameters using very small training data sets, selected from past experimental results of the rotor. The trained ANN models are used to predict the performance data, which are obtained within acceptable error limits. Furthermore, to evaluate the fit values and estimate the variance of the predicted data by the ANN models, linear regression equations are fitted to the experimental and predicted results, which shows that R-squared (R²) values are obtained close to unity meaning good fitting of the data. Moreover, the results of ANN modelling are also compared with that of radial basis function (RBF) networks, which also show a good agreement between ANN predicted data and RBF network data. The present ANN models can be exploited to extract more performance data within a given range of input data.     

Impact of mechanical and chemical weeding on the floristic diversity of citrus orchards in Tlemcen (Northwestern Algeria)

ABSTRACT

This study was conducted to evaluate the impact of weeding (chemical, mechanical and control) on the floristic composition of citrus orchards in the region of Tlemcen (Northwestern Algeria). A comparative approach between two methods of weeding (mechanical vs. chemical) compared to a control (without weeding) was carried out at 3 stations of 100 m² inside the citrus orchard studied. The floristic surveys were performed in the stations at different times (before and after each weeding). In the floristic inventory, 168 surveys were carried out and a total of 88 species were identified belonging to 71 genera and 30 botanical families. This adventitious flora was dominated by the Mediterranean elements (64%), the therophytes (51%) and dicotyledons (64%).

Statistical elaboration of floristic data by means of multifactorial ANOVA revealed the existence of a significant spatial-temporal difference in the mean species richness between the stations (control vs. chemical and mechanical). In addition, the species richness before weeding tended to be higher than that after it. Weeding methods practised by crop growers need to be reconsidered. The benefits that weeds can provide should be considered as well as the damage caused by the different weeding techniques.     

Collector characterisation and heat loss tests on a novel batch solar water heater with CPC reflector for households

ABSTRACT

This solar water-heating unit is an integration of the older concept of batch water heating with the modern trends in solar water-heating technologies i.e. incorporating a concentrator in the design. The concentrator used is the compound parabolic type (CPC) which is a non-imaging device having wider acceptance angle (64°) and supported on a wooden cradle, which comprises the two arms of the parabola. To suppress the heat loss, an air gap has been introduced in the arms of the CPC. The collector is a single larger diameter drum which serves both as an absorber and storage unit positioned at the focus of CPC. The parametric study of the model showed the thermal efficiency of the collector as high as 38% and maximum water temperature attained was 53°C. Heat loss tests performed on the collector on a 24-hr cycle period showed good long time performance estimates. The response time of collector computed and performance characteristic curve plotted to predict system response under any given conditions of solar insolation and ambient temperature.     

Principal component analysis of electricity use in office buildings

Principal component analysis was conducted on five major climatic variables—dry-bulb temperature, wet-bulb temperature, global solar radiation, clearness index and wind speed. Twenty-eight year (1996–2000) long-term measured weather data were considered. A two-component solution was obtained, which could explain 80% of the variance in the original weather data. Monthly electricity consumption data recorded during a 5-year period (1979–2006) were gathered from 20 fully air-conditioned office buildings with centralised HVAC systems in subtropical Hong Kong. Electricity use per unit gross floor area ranged from 163 to 389 kWh/m². These consumption data were correlated with the corresponding principal components using linear multiple regression techniques. The coefficient of determination (R²) varied from 0.76 to 0.95 indicating reasonably strong correlation. It was found that the regression models developed could give a reasonably good indication (mostly within 3%) of the annual electricity use, but the monthly estimates might differ from the actual consumption by up to 9%. Attempt was also made to develop a general regression model for the 20 buildings, which had an R² of 0.84 with a maximum mean-biased error of 18.6% and a maximum root-mean-square error of 21.4%.     

Nestedness pattern and the influence of geographical factors on Algerian island breeding birds

ABSTRACT

The relationship between the abundance of breeding birds on the studied islands and the topographic variables (surface and distance from the continent) was tested via data on the breeding birds of 11 islands in Algeria. A nestedness calculator program was used to study the nestedness of breeding birds. Islands’ surface conditions and their isolation explain clearly the richness of breeding birds. The distribution of island breeding birds showed a strong nested structure that differs significantly from the distribution of the random model.