Solar Irradiance Data Sets Research Articles

PAOFCDN: A novel method for predictive analysis of solar irradiance

Solar photovoltaic power is the most feasible Renewable Energy Source (RES) for Pakistan, due to ample sunlight availability throughout the year. Since solar photovoltaic power is primarily dependent on solar irradiance, forecasting of solar irradiance is essential for reliable, secure and effective incorporation of solar photovoltaic power in power systems. Considering the importance of solar irradiance forecasting, in this study, predictive analysis of Islamabad’s solar irradiance is performed by using a novel proposed model named as Pelican Algorithm-based Optimized Fully-Connected Deep Network (PAOFCDN). The initial weights of Fully-Connected Deep Network (FCDN) are optimized through an effective optimization technique known as Pelican optimization. The accuracy of the optimized network PAOFCDN is enhanced many fold as compared to the FCDN network trained with randomly initialized weights. The inherent issue of poor generalization in FCDN is also resolved by optimization. The superior performance of PAOFCDN is evident from its comparative evaluation with existing benchmark methods, i.e., Long Short-Term Memory (LSTM), Support Vector Regression (SVR), Least Square Boosting (LSBoost) and standard FCDN. PAOFCDN achieves the least Normalized Root Mean Square Error (NRMSE) of 0.0503 as compared to 0.1179 of LSTM, 0.1256 of FCDN, and 0.2992 of SVR and LSBoost. The proposed model is applied to three real-world solar irradiance datasets having different resolutions of 10-minutes, hourly and daily. This study took the initiative of performing predictions on three datsets having multiple resolutions in perspective of south asia.

Validation of satellite-derived solar irradiance datasets: a case study in Saudi Arabia

A robust dataset of Surface Solar Irradiance is essential for secure competitive financing for solar energy projects. Rating agencies and lenders alike require verification of the solar-resource dataset for utilizing each solar energy project, as this can be translated directly into expected electrical energy and revenues. The accuracy of the dataset and the variability of solar radiation, as recorded by historical solar data, play a significant role in estimating the future performance of the project and its budget. The historical observed solar irradiance datasets by local stations are the best and most reliable for a specific site, but they are not always available for long and continuous periods in any location, especially in arid areas. So, the importance of historical solar radiation datasets derived from satellite-based models arises here. This paper validates the historical modeled datasets of the three most famous satellite-based commercial prediction models (SolarGIS, SUNY, and Solcast) against the observed dataset by six ground stations in Saudi Arabia under different climatic zones. The validation method has been implemented using the standard error metrics: Maximum Absolute Error (MAE) and relative Maximum Bias Error (rMBE). The validation process showed that, in the case of GHI, the discrepancy between observed and predicted values is narrow, while in the case of DNI, the discrepancy is wide. Also, the predicted GHI values are more accurate than predicted DNI values, and -in general- the values predicted by the SUNY model are less accurate than those predicted by SolarGIS and Solcast models for both GHI and DNI. The resultant of this validation process could be accepted not for the six locations under study only but, also for deserts and arid areas across Saudi Arabia and might be extended to similar arid areas around the world.

A quality-assured dataset of nine radiation components observed at the Shangdianzi regional GAW station in China (2013–2022)

Abstract. The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at https://doi.org/10.1594/PANGAEA.963330 (Quan et al., 2023b).

Challenges in the Detection and Attribution of Northern Hemisphere Surface Temperature Trends Since 1850

Since 2007, the Intergovernmental Panel on Climate Change (IPCC) has heavily relied on the comparison between global climate model hindcasts and global surface temperature (ST) estimates for concluding that post-1950s global warming is mostly human-caused. In Connolly et al., we cautioned that this approach to the detection and attribution of climate change was highly dependent on the choice of Total Solar Irradiance (TSI) and ST data sets. We compiled 16 TSI and five ST data sets and found by altering the choice of TSI or ST, one could (prematurely) conclude anything from the warming being “mostly human-caused” to “mostly natural.” Richardson and Benestad suggested our analysis was “erroneous” and “flawed” because we did not use a multilinear regression. They argued that applying a multilinear regression to one of the five ST series re-affirmed the IPCC’s attribution statement. They also objected that many of the published TSI data sets were out-of-date. However, here we show that when applying multilinear regression analysis to an expanded and updated data set of 27 TSI series, the original conclusions of Connolly et al. are confirmed for all five ST data sets. Therefore, it is still unclear whether the observed warming is mostly human-caused, mostly natural or some combination of both.

Optimal Capacity of a Battery Energy Storage System Based on Solar Variability Index to Smooth out Power Fluctuations in PV-Diesel Microgrids

Battery energy storage systems can be integrated with photovoltaic (PV)-diesel microgrids as an enabling technology to increase the penetration of PV systems and aid microgrid stability by smoothing out the power fluctuations of the PV systems. This paper focuses on this topic and aims to derive correlations between the optimal capacity of the smoothing batteries and variabilities in the daily solar irradiance. To this end, the two most commonly used moving average and ramp rate control techniques are employed on a real solar irradiance dataset with a 1-min resolution for a full calendar year across 11 sites in Australia. The paper then presents the developed empirical model, based on linear regressions, to estimate the batteries’ optimal capacity without requiring detailed simulation studies, which are useful for practitioners at the early stages of a project’s feasibility evaluation. The performance of the developed technique is validated through numerical simulation studies in MATLAB®. The study demonstrates that the empirical model provided reasonably accurate estimates when using the moving average smoothing technique but had limited accuracy under the ramp rate control technique.

CyL-GHI: Global Horizontal Irradiance Dataset Containing 18 Years of Refined Data at 30-Min Granularity from 37 Stations Located in Castile and León (Spain)

Accurate solar forecasting lately relies on advances in the field of artificial intelligence and on the availability of databases with large amounts of information on meteorological variables. In this paper, we present the methodology applied to introduce a large-scale, public, and solar irradiance dataset, CyL-GHI, containing refined data from 37 stations found within the Spanish region of Castile and León (Spanish: Castilla y León, or CyL). In addition to the data cleaning steps, the procedure also features steps that enable the addition of meteorological and geographical variables that complement the value of the initial data. The proposed dataset, resulting from applying the processing methodology, is delivered both in raw format and with the quality processing applied, and continuously covers 18 years (the period from 1 January 2002 to 31 December 2019), with a temporal resolution of 30 min. CyL-GHI can result in great importance in studies focused on the spatial-temporal characteristics of solar irradiance data, due to the geographical information considered that enables a regional analysis of the phenomena (the 37 stations cover a land area larger than 94,226 km2). Afterwards, three popular artificial intelligence algorithms were optimised and tested on CyL-GHI, their performance values being offered as baselines to compare other forecasting implementations. Furthermore, the ERA5 values corresponding to the studied area were analysed and compared with performance values delivered by the trained models. The inclusion of previous observations of neighbours as input to an optimised Random Forest model (applying a spatio-temporal approach) improved the predictive capability of the machine learning models by almost 3%.

Subordinated Gaussian processes for solar irradiance

AbstractTraditionally the power grid has been a one‐way street with power flowing from large transmission‐connected generators through the distribution network to consumers. This paradigm is changing with the introduction of distributed renewable energy resources (DERs), and with it, the way the grid is managed. There is currently a dearth of high fidelity solar irradiance datasets available to help grid researchers understand how expansion of DERs could affect future power system operations. Realistic simulations of by‐the‐second solar irradiances are needed to study how DER variability affects the grid. Irradiance data are highly non‐stationary and non‐Gaussian, and even modern time series models are challenged by their distributional properties. We develop a subordinated non‐Gaussian stochastic model whose simulations realistically capture the distribution and dependence structure in measured irradiance. We illustrate our approach on a fine resolution dataset from Hawaii, where our approach outperforms standard nonlinear time series models.

A Hybrid Ensemble Learning Model for Short-Term Solar Irradiance Forecasting Using Historical Observations and Sky Images

A hybrid multi-modal ensemble learning model is proposed for short-term solar irradiance forecasting based on historical observations and sky images in this paper. In the proposed model, historical observations of solar irradiance are utilized to extract temporal characteristics, and ground-based sky images are used as an exogenous input to offer cloud cover information. A powerful ensemble learning model, Extreme Gradient Boosting (XGBoost), is employed to capture the function relationships between input features and future observations. Since mean squared error loss is sensitive to the extreme large or small historical irradiance, a novel loss function is proposed to improve robustness of XGBoost. In order to find out the best controlling parameters, Rao-1 algorithm is employed for its easy operation. To validate performance of the proposed method, a solar irradiance dataset containing three-year historical observations and ground-based sky images collected from Folsom is employed. Meanwhile, five commonly applied methods, LASSO, Ridge regression, support vector regression, boosted regression trees, the generic XGBoost, are considered as benchmarking methods. The forecasting horizons from 5 to 30 min are considered for all compared methods while two metrics, mean absolute error and root mean squared error, are computed. Experimental results prove that the proposed hybrid model has better forecasting performance compared with benchmarking methods over all forecasting horizons.

Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar irradiance

Solar energy has become crucial in producing electrical energy because it is inexhaustible and sustainable. However, its uncertain generation causes problems in power system operation. Therefore, solar irradiance forecasting is significant for suitable controlling power system operation, organizing the transmission expansion planning, and dispatching power system generation. Nonetheless, the forecasting performance can be decreased due to the unfitted prediction model and lacked preprocessing. To deal with mentioned issues, this paper proposes Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map (MGEL-ELM) and the Same Datetime Interval Averaged Imputation algorithm (SAME) for improving the forecasting performance of incomplete solar irradiance time series datasets. Thus, the proposed method is not only imputing incomplete forecasting data but also achieving forecasting accuracy. The experimental result of forecasting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coefficient of determination value up to 0.9307 compared to state-of-the-art models. Furthermore, the proposed method consumes less forecasting time than the deep learning model.

A space‐time model with temporal cyclostationarity for probabilistic forecasting and simulation of solar irradiance data

To establish a more sustainable future, Saudi Arabia is striving to reduce its dependency on fossil fuels and promote renewable energies. Solar energy is a major resource in Saudi Arabia because of the country's geographical location with year‐round clear skies and plentiful sunlight. However, although solar energy is a clean and safe renewable energy resource, it can be quite unpredictable. Therefore, solar irradiance needs to be forecasted and simulated as accurately as possible on both regional and national scales in the Kingdom to assist in planning for reserve usage, switching sources and short‐term power purchases. Based on an hourly solar diffuse horizontal irradiance (DHI) dataset from 45 different Saudi Arabian solar monitoring stations, this study proposes a novel spatio‐temporal model. We identify the temporal dependency of DHI as cyclostationary and incorporate this key observation in the model. This feature helps yield accurate (i.e. significantly close to the ground truth and with narrow confidence bands) probabilistic forecasts and realistic simulations in near‐future time points and at new locations. Then, the proposed model is compared with a stationary model that fails to recognize the cyclostationarity structure in the data. The proposed model produces significantly better predictions and simulations than the stationary model. Further, we calculate the photovoltaic power outputs using the simulated samples from both the models and the original observations and then compare them. The simulated samples from the proposed model afford photovoltaic power output estimates that are significantly closer to the original observed data than those from the stationary model, and therefore, they can better assist photovoltaic power output operators in assessing solar energy production and operational pre‐planning to mitigate impacts by the uncertain nature of solar irradiance.

Decarbonizing residential energy consumption under the Italian collective self-consumption regulation

The recent Italian regulatory framework is promoting Collective Self-Consumption to play a key role in the energy transition. In fact, this new scheme allows sharing and exchange of electricity produced from Renewable Energy Sources (RES) among different end-users living in the same multi-family building block. In this context, this paper aims to perform an energy, economic and environmental assessment of creating a RES-based energy community formed by end-users located in the same residential building, taking into account the currently available incentive schemes. In particular, two different progressive scenarios have been analysed: the first one includes only a photovoltaic system to supply the aggregated electricity demand of the apartments, while a heat pump is further integrated in the second scenario for supplying and electrifying/decarbonizing also the space heating demand of the building. Available temperature and solar irradiance datasets at national level were then used to spread the analysis to the whole country, at different latitudes. Although differences exist at regional levels for the proposed scenarios, the results highlight how the RES-based Collective Self-Consumption scheme is economically profitable for Italian residential end-users with cost savings up to 32% and environmentally sustainable with carbon emissions reduction up to 60%. • Economic, energy and environmental analysis of PV and HP for Italian households. • Energy modeling considering solar radiation and air temperature variability with GIS. • The results highlight how the economic and environmental perspectives are positive. • The new regulatory framework concerning energy sharing improve RES diffusion.

A Prefeasibility Solar Photovoltaic Tool for Tropical Small Island Developing States

Small island developing states (SIDS) are the lowest emitters of greenhouse gases yet are the most vulnerable to the impacts of global climate warming. Many islands, such as the Caribbean islands, identified solar photovoltaics as a technology for reducing greenhouse gas emissions from their electricity sector. However, prefeasibility economic studies for photovoltaics are challenging as operational photovoltaic system data are nonexistent, and the measured solar radiation datasets are limited. Thus, a prefeasibility PV tool that uses ground-measured global horizontal irradiation and a supplementary photovoltaic derating factor model is proposed for use in tropical SIDS. In addition, the bias of a modelled irradiation dataset was quantified with limited solar radiation data for a tropical Caribbean SIDS, Trinidad and Tobago. For this SIDS, the tool estimates the annual energy output of a 50 MW photovoltaic system to be 57,890 MWh and the levelized cost of electricity to be USD 0.12/kWh. The performance of the proposed tool was comparable with two existing prefeasibility models, RETScreen and SAM, which use past ground measurements and modelled data, respectively. The biases in the annual irradiation data for RETScreen and SAM were determined to be 6% and 25%, respectively, against the solar irradiance dataset used. The proposed tool may be useful for first approximation prefeasibility photovoltaic studies in similar regions with limited climatic data.

Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm

Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting model based on three steps. In the first step, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In the second step, unlike the traditional deep learning models designing their architectures manually, we utilize several deep long short term memory-convolutional neural network (LSTM-CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally, in the third step, we deploy a deep reinforcement learning strategy for selecting the best subset of the combined deep optimized LSTM-CNN models. Through analysing the forecasting results over two real-world datasets gathered from the USA solar irradiance stations, it can be inferred that our proposed algorithm outperforms other powerful benchmarked algorithms in 1-step, 2-step, 12-step, and 24-step ahead forecasting.

Comprehensive assessment, review, and comparison of AI models for solar irradiance prediction based on different time/estimation intervals

Solar energy-based technologies have developed rapidly in recent years, however, the inability to appropriately estimate solar energy resources is still a major drawback for these technologies. In this study, eight different artificial intelligence (AI) models namely; convolutional neural network (CNN), artificial neural network (ANN), long short-term memory recurrent model (LSTM), eXtreme gradient boost algorithm (XG Boost), multiple linear regression (MLR), polynomial regression (PLR), decision tree regression (DTR), and random forest regression (RFR) are designed and compared for solar irradiance prediction. Additionally, two hybrid deep neural network models (ANN-CNN and CNN-LSTM-ANN) are developed in this study for the same task. This study is novel as each of the AI models developed was used to estimate solar irradiance considering different timesteps (hourly, every minute, and daily average). Also, different solar irradiance datasets (from six countries in Africa) measured with various instruments were used to train/test the AI models. With the aim to check if there is a universal AI model for solar irradiance estimation in developing countries, the results of this study show that various AI models are suitable for different solar irradiance estimation tasks. However, XG boost has a consistently high performance for all the case studies and is the best model for 10 of the 13 case studies considered in this paper. The result of this study also shows that the prediction of hourly solar irradiance is more accurate for the models when compared to daily average and minutes timestep. The specific performance of each model for all the case studies is explicated in the paper.

Modeling combined global, beam, and diffuse clear-sky indices with Markov-chain mixture distribution models

This study uses the N-state Markov-chain mixture distribution model and the multiple-component N-state Markov-chain mixture distribution model to simulate global, beam, and diffuse horizontal clear-sky index. The models are data-driven such that when trained on single or multiple clear-sky index time-series, the models generate arbitrarily long synthetic clear-sky index time-series for the same components. The models were tested on solar irradiance datasets from two different climatic regions: Norrköping, Sweden, and Oahu, Hawaii, USA. The results show high probability distribution and temporal autocorrelation goodness-of-fit for all models and high cross correlation goodness-of-fit as well as accurate correlation between the component datasets for the multiple component model simulations. When combined with, e.g., the Hay and Davies model, the output from this model could, for example, be used to generate realistic time-series of incident solar irradiance on tilted planes.

Solar Resource Potentials and Annual Capacity Factor Based on the Korean Solar Irradiance Datasets Derived by the Satellite Imagery from 1996 to 2019

The Korea Institute of Energy Research builds Korean solar irradiance datasets, using gridded solar insolation estimates derived using the University of Arizona solar irradiance based on Satellite–Korea Institute of Energy Research (UASIBS–KIER) model, with the incorporation of geostationary satellites over the Korean Peninsula, from 1996 to 2019. During the investigation period, the monthly mean of daily total irradiance was in a good agreement with the in situ measurements at 18 ground stations; the mean absolute error is also normalized to 9.4%. It is observed that the irradiance estimates in the datasets have been gradually increasing at a rate of 0.019 kWh m−2 d−1 per year. The monthly variation in solar irradiance indicates that the meteorological conditions in the spring season dominate the annual solar insolation. In addition, the local distribution of solar irradiance is primarily affected by the geographical environment; higher solar insolation is observed in the southern part of Korea, but lower solar insolation is observed in the mountainous range in Korea. The annual capacity factor is the secondary output from the Korean solar irradiance datasets. The reliability of the estimate of this factor is proven by the high correlation coefficient of 0.912. Thus, in accordance with the results from the spatial distribution of solar irradiance, the southern part of Korea is an appropriate region for establishing solar power plants exhibiting a higher annual capacity factor than the other regions.

An Improved Solar Spectral Irradiance Composite Record

AbstractThe solar spectral irradiance (SSI) data set is a key record for studying and understanding the energetics and radiation balance in Earth’s environment. Understanding the long‐term variations of the SSI over time scales of the 11‐years solar activity cycle and longer is critical for many Sun‐climate research topics. There are satellite measurements of the SSI since the 1970s that contribute to understanding the solar variability over Solar Cycles (SC) 21–24, with most of these SSI measurements in the ultraviolet and only recently in the visible and near‐infrared for SC‐23 and SC‐24. A limiting factor for the accuracy of the previous results is the uncertainties for the instrument degradation corrections. Analyses of the past SSI data sets have identified some irradiance offsets and some small residual instrumental trends. These corrections are applied and then combined with a previous SSI composite data set, called the GSFCSSI2 composite, to provide a new SSI composite, called the LASP GSFC SSI #3 (or SSI3). This improved composite extends the wavelength coverage down to 0.5 nm and up to 1,600 nm and the time coverage up to 2020. The solar variability results from the SSI3 are consistent, of course, with the observations that are used to create the SSI3, but they do differ with some solar variability models, in particular at wavelengths longer than 900 nm. The development of the SSI3 composite also clarifies the importance of overlapping missions for studying the 11‐years solar activity cycle, especially so for wavelengths longer than 200 nm.

Quantifying the Impact of Solar Spectra on the Inter-Calibration of Satellite Instruments

In satellite-based remote sensing applications, the conversion of the sensor recorded top-of-atmosphere reflectance to radiance, or vice-versa, is carried out using a reference spectral solar irradiance (SSI) dataset. The choice of reference SSI spectrum has consistently changed over the past four decades with the increasing availability of more accurate SSI measurements with greater spectral coverage. Considerable differences (up to 15% at certain wavelengths) exist between the numerous SSI spectra that are currently being used in satellite ground processing systems. The aim of this study is to quantify the absolute differences between the most commonly used SSI datasets and investigate their impact in satellite inter-calibration and environmental retrievals. It was noted that if analogous SNPP and NOAA-20 VIIRS channel reflectances were perfectly inter-calibrated, the derived channel radiances can still differ by up to 3% due to the utilization of differing SSI datasets by the two VIIRS instruments. This paper also highlights a TSIS-1 SIM-based Hybrid Solar Reference Spectrum (HSRS) with an unprecedented absolute accuracy of 0.3% between 460 and 2365 nm, and recommends that the remote sensing community use it as a common reference SSI in satellite retrievals.

SOLAR-v: A new solar spectral irradiance dataset based on SOLAR/SOLSPEC observations during solar cycle 24

Context. Solar spectral irradiance (SSI) is the wavelength-dependent energy input to the top of the Earth’s atmosphere. Solar ultraviolet (UV) irradiance represents the primary forcing mechanism for the photochemistry, heating, and dynamics of the Earth’s atmosphere. Hence, both temporal and spectral variations in solar UV irradiance represent crucial inputs to the modeling and understanding of the behavior of the Earth’s atmosphere. Therefore, measuring the long-term solar UV irradiance variations over the 11-year solar activity cycle (and over longer timescales) is fundamental. Thus, each new solar spectral irradiance dataset based on long-term observations represents a major interest and can be used for further investigations of the long-term trend of solar activity and the construction of a homogeneous solar spectral irradiance record. Aims. The main objective of this article is to present a new solar spectral irradiance database (SOLAR-v) with the associated uncertainties. This dataset is based on solar UV irradiance observations (165−300 nm) of the SOLAR/SOLSPEC space-based instrument, which provides measurements of the full-disk SSI during solar cycle 24. Methods. SOLAR/SOLSPEC made solar acquisitions between April 5, 2008 and February 10, 2017. During this period, the instrument was affected by the harsh space environment that introduces instrumental trends (degradation) in the SSI measurements. A new method based on an adaptation of the Multiple Same-Irradiance-Level (MuSIL) technique was used to separate solar variability and any uncorrected instrumental trends in the SOLAR/SOLSPEC UV irradiance measurements. Results. A new method for correcting degradation has been applied to the SOLAR/SOLSPEC UV irradiance records to provide new solar cycle variability results during solar cycle 24. Irradiances are reported at a mean solar distance of 1 astronomical unit (AU). In the 165−242 nm spectral region, the SOLAR/SOLSPEC data agrees with the observations (SORCE/SOLSTICE) and models (SATIRE-S, NRLSSI 2) to within the 1-sigma error envelope. Between 242 and 300 nm, SOLAR/SOLSPEC agrees only with the models.

Solar infrared radiation towards building energy efficiency: measurement, data, and modeling

With the recent discoveries and engineering solutions emerging in nanomaterials and nanostructures, independent band modulation of solar radiation on building envelopes, including glazing systems, has become increasingly viable as a potential means of improving building energy savings and indoor visual comfort. However, when it comes to the prediction of these new materials’ potential energy performance in buildings, most studies utilize a simple solar irradiance (e.g., global horizontal solar irradiance, direct beam solar irradiance) or a rough estimation of solar infrared (e.g., 50% solar irradiance) as input, which may cause significant errors. Consequently, there is a pressing need for reliable performance estimations of the solar infrared control and response at the building’s scale. To assess this, we need a solar spectral irradiance model, or at least a wideband (visible or infrared) solar irradiance model, as input. To develop this new type of model, one needs to understand the modeling-related key elements, including available solar spectral irradiance datasets, data collection methods, and modeling techniques. As such, this paper reviews the current major measurement methods and tools used in collecting solar spectral irradiance data with a focus on the solar infrared region, identifies the available related resources and datasets that particularly encompass the solar spectral irradiance data with a sufficient wavelength range, and studies existing solar irradiation modeling techniques for building simulations. These investigations will then form the background and backbone for a study scheme of solar infrared radiation modeling and indicate future research paths and opportunities.