Global Horizontal Irradiance Research Articles

Multi‐Criteria Decision Analysis‐Based Solutions for the Installation of Photovoltaic (PV) Solar Power Plants in an Energy Deficit State of India: An Effort Toward SDG‐7 (Affordable and Sustainable Energy)

ABSTRACTSustainable Development Goal‐7 (SDG‐7) of the United Nations promotes the use of renewable and affordable energy. Solar energy holds significant promise as an effective and affordable renewable source in tropical countries. GIS‐based Multi‐Criteria Decision Analysis (MCDA) approach has been applied to evaluate land suitability for installing ground‐mounted and grid‐connected solar photovoltaic power plants in Purulia district of West Bengal (India) to satisfy the growing demand of energy in an eco‐friendly manner. The study reveals that variation in Global Horizontal Irradiance (GHI) is the most relevant parameter for installing solar PV power plants in the district among the 13 physical parameters, followed by the aspect, slope, and proximity to the grid. The site‐suitable map created using the weights of these factors has revealed that about 33.4% of the district's total land area is ideal for installing solar PV power plants, with most of these areas being in the western and central regions.

GIS-based multi-criteria approach for ranking suitable areas for the implementation of PV-Powered MCDI desalination plants in the Rheris Watershed, Southeast Morocco

In Morocco's oases, which rely heavily on surface water from mountain sources, the threat of decreasing rainfall presents a significant challenge, leading to a decline in surface water availability. This study focuses on the Rheris Watershed in Southeast Morocco, where brackish water desalination emerges as a viable alternative to ensure sustainable water supply for agriculture. Prior to the deployment of a pilot plant integrating a desalination unit, selecting an optimal site for PV-powered MCDI desalination plants was critical. This selection was based on a range of criteria, including Groundwater Quantity, Global Horizontal Irradiation, Temperature, Evaporation, Water Salinity, Water Quantity, Well Density and Slope. These criteria were evaluated using a combination of the Analytical Hierarchy Process (AHP), and geospatial data obtained from Remote Sensing (RS) and Geographic Information System (GIS) techniques.This case study in the Rheris watershed identified potential sites for future Membrane Capacitive Deionization (MCDI) desalination projects, High suitability areas encompass 280.45 km² (30.91 %), located west of Tinejdad, Khettarat Moulay Hachem, north of Goulmima, and south of Tinghir. Low suitability areas cover 63.75 km² (7.02 % of the study area), including regions south of Tadighoust and Tinejdad. Moderate suitability areas span 562.86 km² (62.05 %), involving central regions of Goulmima, Tadighoust, Tinejdad, and Tinghir. Based on the results of the study, the recommendations include focusing on areas identified as highly suitable for MCDI technology for sustainable agriculture, refining selection criteria based on local data, exploring brine management strategies, and adopting integrated water management approaches to ensure sustainable resource use.

Analysis of WRF-solar in the estimation of global horizontal irradiation in Amapá, northern Brazil

Global horizontal irradiance (GHI) was simulated using version 4.3.1 of the WRF-Solar model over the state of Amapá in the northern region of Brazil. The performance of the Fast All-sky Model for Solar Applications (FARMS) algorithm, called WsolarAPf, was evaluated. The algorithm proposed by the Baseline Surface Radiation Network was used to treat hourly data from the automatic meteorological station of the National Institute of Meteorology in the city of Macapá-AP. Two cumulus parameterizations were needed to represent the rainy and less rainy periods. The modeling used input data from ERA5 reanalysis. Using cluster analysis, it was possible to estimate the typical meteorological year representative for a large area. The use of the FARMS algorithm reduced the relative RMSE of the GHI during winter (JJA) and austral spring (SON), from 26 % to 16 % in austral winter and from 17 % to 12 % in austral spring. The average daily maximum of GHI occurs at 13:00 local time during all seasons, and WRF-Solar showed it at 13:00 and 14:00 in the rainy season and 12:00 and 13:00 in the less rainy season. The suggestion to explore the development of an optimal combination of the two model predictions can be implemented using FARMS.

Evaluation of a solar-driven adsorption desalination system for Brazilian semiarid region

In this study, the effect of using a hybrid solar thermal-activated adsorption desalination system for brackish water is evaluated under the climatic conditions of the Brazilian semiarid region. The proposed theoretical model utilizes climatic data from the meteorological station in Campina Grande, PB, and adsorptive kinetics data of Fuji Davison RD 260 silica gel to predict the performance indices of the specific daily water production (SDWP), specific cooling power (SCP), and coefficient of performance (COP) performance coefficients over a characteristic day. The SDWP value of 6.26 m3/ton, SCP ranging from 50 to 300 W/kg, and an average COP of 0.5 were obtained, considering variations in global horizontal irradiance in the ACDS system and transient ambient temperature. It was observed that both the production of desalinated water and the refrigeration effect increase with the rise in daily solar irradiance. The variation in the number of solar collectors used in the system and their optimality, as well as the variation in the salinity index of the feed source, impacted the evaluated performance coefficients.

Site selection of floating photovoltaic systems on hydropower reservoirs using fuzzy sine trigonometric decision-making model: Turkey as a case study

Floating photovoltaic systems are a promising option for renewable energy by mitigating the drawbacks of land-based photovoltaic systems. However, renewable energy systems are designed in a site-specific way, therefore, the performance of renewable energy alternatives substantially depends on the selected site characteristics. In this study, a comprehensive assessment was performed to identify the optimal hydroelectric power plant reservoir for floating photovoltaic system deployment in Turkey using the Geographical Information System and Multi-Criteria Decision Making method. A two-staged methodology was used to evaluate the results of questionnaires including several expert opinions. In the first stage, a nonlinear fuzzy Aczel-Alsina functions based Ordinal Priority Approach method was employed to weigh the fourteen sub-criteria based on technical, environmental, economic and social considerations. In the second stage, fuzzy sine Trigonometric based Additive Ratio Assessment method was used to rank the fifteen hydroelectric power plant reservoirs all across Turkey. Despite the global horizontal irradiation being the most important criterion, Sarıyar hydroelectric power plant reservoir, with one of the lowest global horizontal irradiation values among alternatives, was determined as the most suitable dam reservoir for floating photovoltaic installation in Turkey. Therefore, the findings of this study highlight the importance of comprehensive analysis that considers the several assessment criteria from fundamental aspects to achieve reliable site selection for floating photovoltaic systems.

Ultra-short-term global horizontal irradiance forecasting based on a novel and hybrid GRU-TCN model

The need for energy is increasing globally due to a several factors, including population growth and economic development. Achieving this energy demand in the face of global warming and the depletion of fossil fuels requires the use of renewable energy. Photovoltaic energy is one of the renewable energy sources that is widely used in many nations across the world. Photovoltaic (PV) energy integration into the grid has significant benefits for the environment and economy, but at high penetration levels, its intermittent nature makes system stability difficult to maintain. Accurate ultra-short-term global horizontal irradiance forecasting is necessary in order to guarantee the most optimal use of photovoltaic power production sources. For GHI forecasting, a novel GRU-TCN-based model is proposed in this paper. It is composed of two neural networks: a temporal convolutional network and a gated recurrent unit. After extracting the temporal features from time-series solar irradiance data using GRU, the spatial features are obtained from the correlation matrix of different meteorological variables of the target and its neighbor position using TCN. Univariate and multivariate GRU-TCN models have been used for GHI ultra short-term forecasting. This paper compares the univariate and multivariate GRU-TCN models with TCN, LSTM, and GRU models based on three evaluation metrics in order to investigate how different combinations of variables affect the accuracy of the GRU-TCN models for one-step forecasting. The findings indicate the adoption of a univariate model using historical data of GHI is suitable to obtain reliable forecasting with 23.02 (W/m2) in MAE as opposed to the best multivariate model that achieved 25.67 (W/m2) in MAE. According to the results, the proposed model outperforms the other models assessed and offers a practical alternative for ultra-short-term GHI forecasting.

Empowering the solar energy landscape: The techno-economic analysis of grid-connected PV power plants in Uganda

Solar PV power is still under-utilized despite the abundance of solar radiation in Uganda. There is need for empowering renewable energy landscape through unlocking the technical and economic feasibility of solar photovoltaic power. We analyzed data from 56 locations for the techno-economic and environmental assessment of photovoltaic power facilities in Uganda. This was based on weather data availability and accessibility to the national power grid. Analysis of the energy generation and different input factors was done using PVsyst 7.2. A three stage approach to losses was adopted: absorption of sunlight, conversion to DC and DC to AC conversion. Findings indicate that most of the countryside is suitable for construction of large scale grid-connected photovoltaic power facilities. Due to longer sunshine duration and stronger Global Horizontal Irradiance (GHI) which are associated with high energy yield, northern Uganda performed better than the rest of the country, making it a preferential siting for large scale grid-connected photovoltaic facilities. South western Uganda performed the poorest. After a thorough energy accounting and a list of all performance metrics, the viability of investing in grid-connected photovoltaic power facilities was assessed.

Statistical Analysis of Annual DNI Distribution and its Impact on Bankability Assessment for Concentrated Solar Power Plants

The Weibull distribution is commonly accepted as the most suitable model for describing the annual distribution of Direct Normal Irradiance (DNI). However, when the annual DNI is assumed to follow a Normal distribution instead of a Weibull distribution, there is a notable increase in the cumulative annual values for unfavourable and worst-case scenarios. In this research, we assess the suitability of different statistical indexes for goodness-of-fit by applying them to the annual cumulative DNI and Global Horizontal Irradiance (GHI) data recorded at six locations with varying climates. Our observations reveal that, across all locations, the Representative Solar Year (RSY) aligns with the 50% Probability of Exceedance (PoE50) of a Normal distribution fitted to the observed data. We quantify that assuming the annual DNI conforms to a Weibull distribution, as opposed to a Normal distribution, results in a substantial decrease of approximately 7% in the annual cumulative value for the worst-case scenario. Based on our analysis, we find no compelling evidence to reject the hypothesis that the annual DNI follows a Normal distribution.

Improving the National Solar Radiation Database (NSRDB) Using a Physics-Based Direct Normal Irradiance (DNI) Model

The National Solar Radiation Database (NSRDB) is a widely used resource providing satellite-derived solar data across the United States and globally. While the NSRDB employs a physical model for computing global horizontal irradiance (GHI), its current method for estimating cloudy-sky direct normal irradiance (DNI) relies on surface observations and empirical models. Recently, a novel physics-based approach, the Fast All-Sky Radiation Model for Solar applications with DNI (FARMS-DNI), was developed to enhance the DNI forecasting. FARMS-DNI incorporates both direct and scattered solar radiation within the circumsolar region, resulting in improved day-ahead DNI predictions when integrated into the Weather Research and Forecasting model with Solar extensions (WRF-Solar). This study integrates FARMS-DNI into the NSRDB algorithm to generate high-resolution DNI data from satellite resources. Our findings reveal that FARMS-DNI effectively mitigates the substantial DNI overestimation present in the conventional NSRDB across surface sites, particularly in conditions categorized as cloudy overcast. Consequently, this innovative model substantially enhances the overall accuracy of the NSRDB.

Comparative Analysis of Solar Radiation Forecasting Techniques in Zacatecas, Mexico

This work explores the prediction of daily Global Horizontal Irradiance (GHI) patterns in the region of Zacatecas, Mexico, using a diverse range of predictive models, encompassing traditional regressors and advanced neural networks like Evolutionary Neural Architecture Search (ENAS), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Meta’s Prophet. This work addressing a notable gap in regional research, and aims to democratize access to accurate solar radiation forecasting methodologies. The evaluations carried out using the time series data obtained by Comisión Nacional del Agua (Conagua) covering the period from 2015 to 2018 reveal different performances of the model in different sky conditions, showcasing strengths in forecasting clear and partially cloudy days while encountering challenges with cloudy conditions. Overall, correlation coefficients (r) ranged between 0.55 and 0.72, with Root Mean Square Error % (RMSE %) values spanning from 20.05% to 20.54%, indicating moderate to good predictive accuracy. This study underscores the need for longer datasets to bolster future predictive capabilities. By democratizing access to these predictive tools, this research facilitates informed decision-making in renewable energy planning and sustainable development strategies tailored to the unique environmental dynamics of the region of Zacatecas and comparable regions.

Algorithmic advancements in agrivoltaics: Modeling shading effects of semi-transparent photovoltaics

Radiation is a crucial factor in the field of agrivoltaics in greenhouses. Depending on the type of photovoltaics integrated into greenhouses, the effect on radiation varies through the phenomenon of shading. Shading in greenhouses can be either beneficial or detrimental, making its analysis imperative. In this study, the improvements and modifications made in an algorithm capable of calculating the shading from photovoltaic units installed in greenhouses are presented. A key modification to the algorithm is the calculation of shading from semi-transparent photovoltaic modules, in contrast to its original form, in which photovoltaic modules were considered opaque. The algorithm was validated using radiation data from pyranometers within a greenhouse. The coefficients used were Pearson's r correlation coefficient and the Coefficient of Variation. The correlation coefficients for times without shading effect approached the values for the case without photovoltaics installed on the roof. Simultaneously, based on the coefficients of variation, the uniformity of radiation within the shade was validated, thereby confirming its existence. Finally, the effect of semi-transparent photovoltaic units on Global Horizontal Irradiance and Photosynthetically Active Radiation was studied, with the reduction for the former approaching 52 % and about 60 % for the latter. These changes in radiation can be either beneficial or not, depending on the type of crop and the needs of the greenhouse, such as cooling.

Accurate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparative Study of Machine Learning Predictive Models and Feature Selection Techniques

Accurate Forecasting of Global Horizontal Irradiance in Saudi Arabia: A Comparative Study of Machine Learning Predictive Models and Feature Selection Techniques

Microclimate Vision: Multimodal prediction of climatic parameters using street-level and satellite imagery

High-resolution microclimate data is essential for capturing spatio-temporal heterogeneity of urban climate and heat health management. However, previous studies have relied on dense measurements that require significant costs for equipment, or on physical simulations demanding intensive computational loads. As a potential alternative to these methods, we propose a multimodal deep learning model to predict microclimate at a high spatial and temporal resolution based on street-level and satellite imagery. This model consists of LSTM and ResNet-18 architectures, and predicts air temperature (Tair), relative humidity (RH), wind speed (ν), and global horizontal irradiance (GHI). For our study area situated at a university campus in Singapore, we collected microclimate data, street-level and satellite imagery. We conducted extensive experiments with our collected dataset to showcase our model’s predictive capabilities and its practical use in generating high-resolution microclimate maps. Our model reported RMSE at 0.95 °C for Tair, 2.57% for RH, 0.31 m/s for ν, and 225 W/m2 for GHI. Furthermore, we observed a contribution of imagery inputs to higher accuracy by comparing models with and without such inputs. We identified hot spots at a high spatio-temporal resolution, indicating its application for issuing real-time heat alerts. Our models are released openly at the microclimate-vision GitHub repository (https://github.com/kunifujiwara/microclimate-vision).

A radiant shift: Attention-embedded CNNs for accurate solar irradiance forecasting and prediction from sky images

The continuous increase in solar power integration with energy systems can be attributed to the push for cleaner energy use globally, highlighting the importance of accurate solar forecasts. Conventional prediction methods, although valuable, often fall short of delivering the precision required for dynamic energy management systems due to their inability to effectively capture the intricate relationships inherent in solar irradiance variations. This research addresses this limitation by introducing a novel approach that harnesses the potential of Attention-embedded Convolutional Neural Networks (ATT_CNN) to forecast Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI) for intra-hour solar forecasting utilizing sequences of sky images. The main contribution of this study is the integration of attention mechanisms into the CNN architecture, strategically designed to enhance their efficacy in predicting GHI, DNI, and DHI by fostering an adaptive focus on sky image features. First, we use SRRL dataset with GHI, DNI, and DHI features as predicting labels, then the Mean Bias Error (MBE), Root Mean Square Error (RMSE), and Forecasting RMSE skill score (FSS) are used to evaluate the model's performance. This study utilized six lead times and four sequence lengths, based on this the best combination is: Sequence length: 4 with Minutes: 20. This combination provides a balanced and optimal performance with low RMSE (62.75 W/m2), low MBE (2.71 W/m2), and a high FSS (38.81), indicating good accuracy, minimal bias, and high skill score. Furthermore, when compared with state-of-the-art models, the proposed model yielded superior results. This advancement holds profound implications for the optimization of solar power utilization within mainstream energy systems, further underscoring the significance of cutting-edge deep learning (DL) techniques in advancing sustainable energy technologies. The findings of this study indicate that integrating attention mechanisms is essential to enhance the accuracy and reliability of forecasts, and using longer sequences of images can further improve forecasting performance.

Robust estimation of global horizontal irradiance with modified fuzzy regression functions with a noise cluster in Australia

The utilization of solar energy is picking up speed to counter climate change. New large-scale photovoltaic power stations are being constructed to increase solar utilization in the energy mix. A critical input of site selection for solar farms is the solar energy generation potential at a given location. Various physical and satellite-based inversion models are proposed to estimate the solar irradiation reaching the ground at potential locations, based on the meteorological features. However, the meteorological features generally contain outlier observations that distract the solar irradiation estimation models. To address this challenge, this study employs a robust fuzzy regression functions framework against the outliers to estimate the global horizontal irradiance (GHI) in Australia. Our framework is benchmarked with support vector machines, deep neural networks, and an adaptive network-based fuzzy inference system, and better GHI estimation performance is observed. The proposed framework provides 24 %, 18 %, and 23 % gain over the second-best method in terms of the rescaled mean absolute error, absolute percentage bias and rescaled root-mean-squared error. Monthly and annual GHI maps are created for Australia and compared to those from NASA POWER GHI estimates and Solargis annual GHI estimates. Our framework has an error range between 0.075 % and 2.9 % when validated against ground measurements. It provides at least an average of 40% lower monthly and annual error rates than POWER. This rate of gain rises to 69% when compared to Solargis. Our maps are not impacted by terrestrial characteristics and clear-sky conditions. This study’s results are beneficial in site selection and construction of high-precision GHI estimation models for practitioners.

Evaluation and correction of solar irradiance in Somaliland using ground measurements and global reanalysis products

Properly understanding solar irradiance can help accurately quantify the solar energy resource and guide sustainable development projects, particularly where measured solar data are scarce or suffer from detrimental data quality issues. This study aims to assess and improve solar global horizontal irradiance (GHI) data from a diverse range of global reanalysis datasets by utilizing measured data from two ground weather stations located in Somaliland. A comprehensive evaluation framework is employed, combining various statistical and regression error metrics, whereas bias correction methods are implemented. The comparative study covers several analytical facets such as the hourly, daily and monthly data analysis before and after correction along with analyzing the seasonal variations, clear-sky and all-sky conditions. The analysis revealed pattern of an overall underestimation of GHI with varying degrees of accuracy in the estimated GHI datasets before and after correction. The annual ranges for rMBE, rMAE, rRMSE and R2 extend from 3–31%, 12–33%, 19–53% and 0.797–0.979, respectively, across all datasets for six-hourly data in the two observed stations. Following bias correction, the ranges for rMBE, rMAE, rRMSE reduce to is 0%, 8–31%, 11–34% and R2 increase to 0.897–0.984 respectively. While certain datasets such as MERRA-2 and SARAH-2 demonstrate close alignment with ground data before correction, other especially ERA5-Land exhibit exceptional improvement after the bias correction.

Future scenario generation and reduction methods for solar photovoltaic electricity production analysis

Abstract A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.

Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Stations Based on Dynamic Variable Weight

Multimodel Ensemble Forecast of Global Horizontal Irradiance at PV Stations Based on Dynamic Variable Weight

Planning a Hybrid Battery Energy Storage System for Supplying Electric Vehicle Charging Station Microgrids

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

Irradiance separation model parameter estimation from historical cloud cover statistical properties

Irradiance separation models allow the decomposition of Global Horizontal Irradiance into Diffuse Horizontal and Direct Normal Irradiances. These models need fitting to the irradiance characteristics of the location of interest using locally measured ground data. For locations that only measure Global Horizontal Irradiance, current state of the art establishes the use of parameters obtained for another location that measures the three components, with similar climate characteristics. Nevertheless, this results in a lack of localized character for estimates and requires fitting model parameters for all possible climates, which can be infeasible given data availability. This work presents a novel approach based on the hypothesis that the separation model's parameters are a function of the statistical properties of satellite-derived cloud cover estimates. The proposed methodology was evaluated in 23 sites covering all main Köppen-Geiger climatic types and different cloud coverage properties using the Boland-Ridley-Lauret diffuse fraction model. The model performs similarly as locally adjusting the model, with Root Mean Square Errors of 0.087–0.15 diffuse fraction units versus 0.077–0.127 for locally optimized parameters, and offers adequate performance across climates and cloud characteristics. These results encourage future research by generalizing parameter estimation for other diffuse fraction models. The main applications for this research are the estimation of irradiance components where no local data is available for model fitting and the enhancement or complementarity of satellite estimates of surface irradiance. Furthermore, it allows the estimation of missing irradiance components due to equipment failure in locations with insufficient data for a representative, locally adapted model.