Article
Computer Science, Information Systems
Supachai Suksamosorn, Naebboon Hoonchareon, Jitkomut Songsiri
Summary: The study aimed to develop a solar power forecasting model suitable for a tropical climate, using Thailand as a model, and proposed a linear recursive regression model to reduce errors obtained from the WRF model. By utilizing stepwise regression and a Kalman filtering scheme, the forecasting errors of the WRF model were decreased by 7-12% on average, leading to improved prediction accuracy.
Article
Energy & Fuels
Pedro A. Jimenez, Jimy Dudhia, Gregory Thompson, Jared A. Lee, Thomas Brummet
Summary: In this study, a new MAD-WRF model is proposed by combining the MADCast model with the WRF-Solar model, which can improve short-term predictions by integrating cloud initialization and cloud physics parameterization. The experimental results demonstrate that the MAD-WRF model performs better in global horizontal irradiance predictions.
Article
Energy & Fuels
Benedikt Schulz, Mehrez El Ayari, Sebastian Lerch, Sandor Baran
Summary: Probabilistic energy forecasting is crucial for integrating volatile power sources like solar energy into the electrical grid. Hybrid models combining physical and statistical methods have shown to be effective, with post-processing models proving to significantly improve the forecast performance of ensemble predictions and correct systematic biases.
Article
Physics, Multidisciplinary
Mawloud Guermoui, Kada Bouchouicha, Said Benkaciali, Kacem Gairaa, Nadjem Bailek
Summary: This study proposes a new machine learning forecasting architecture, including a decomposition-based ensemble-forecasting model, for effective solar irradiance forecasting in photovoltaic technology. By combining a new multi-scale decomposition algorithm with Gaussian Process Regression, a forecasting model called IF-GPR is developed. The performance of the model is validated using hourly solar radiation data from different cities in Algeria, demonstrating its potential for multi-hour forecasting. The proposed IF method proves to be superior to other decomposition algorithms in enhancing the forecasting ability of a stand-alone model.
EUROPEAN PHYSICAL JOURNAL PLUS
(2022)
Article
Energy & Fuels
Hadrien Verbois, Yves-Marie Saint-Drenan, Alexandre Thiery, Philippe Blanc
Summary: The share of solar power in the global and local energy mixes has significantly increased in the past decade, leading to a rise in interest for solar power forecasting. Numerical Weather Prediction (NWP) models and post-processing algorithms are the most popular methods for day-ahead forecasts. However, comparing results across different studies is challenging due to variations in datasets, metrics, and cross-validation methods. This study proposes a rigorous benchmark of solar NWP post-processing models using an open dataset spanning 6 years and 7 locations. The results demonstrate the systematic benefits of using large predictor sets with proper regularization, as well as the superior performance of more complex algorithms such as neural networks and gradient boosting in terms of mean square error. Support vector regression, a more parsimonious algorithm, performs better in terms of mean absolute error. The study highlights the importance of considering systematic ranking when evaluating forecasting models and emphasizes that no single model is superior in all situations.
Article
Meteorology & Atmospheric Sciences
Robert Huva, Guiting Song, Xiaohui Zhong, Yangyang Zhao
Summary: Numerical weather prediction models have various options for handling processes that cannot be explicitly resolved, which is a continuing focus in atmospheric science research. Optimizing the configuration of the WRF model based on weather type shows a 13.6% improvement over using a single best configuration, and this performance gain holds true for a longer 3-month test period with a 17.8% improvement.
METEOROLOGICAL APPLICATIONS
(2021)
Article
Energy & Fuels
Pravat Kumar Ray, Bidyadhar Subudhi, Ghanim Putrus, Mousa Marzband, Zunaib Ali
Summary: This paper presents a novel approach to forecast global insolation and utilizes measurements from a global positioning system (GPS) to determine parameters such as latitude and precipitable water content. The model is verified and validated using data from various locations, and the performance is compared with other popular algorithms. The results show high accuracy and effectiveness in estimating global solar insolation.
CSEE JOURNAL OF POWER AND ENERGY SYSTEMS
(2022)
Article
Chemistry, Multidisciplinary
Domingos S. de O. Jr Jr Santos, Paulo S. G. de Mattos Neto, Joao F. L. de Oliveira, Hugo Valadares Siqueira, Tathiana Mikamura Barchi, Aranildo R. Lima, Francisco Madeiro, Douglas A. P. Dantas, Attilio Converti, Alex C. Pereira, Jose Bione de Melo Filho, Manoel H. N. Marinho
Summary: Solar irradiance forecasting is crucial for renewable energy generation, as it enhances the planning and operation of photovoltaic systems. Traditional single models may underperform due to inappropriate selection, misspecification, or random fluctuations. This research proposes a heterogeneous ensemble dynamic selection model that outperforms single models in terms of accuracy.
APPLIED SCIENCES-BASEL
(2022)
Article
Thermodynamics
J. A. Sward, T. R. Ault, K. M. Zhang
Summary: To enable a future run by renewable energy, designing power systems to efficiently capture, store, and transmit solar and wind energy is essential. Integrating meteorological and power systems modeling is crucial in achieving this goal. Optimizing WRF model physics using a genetic algorithm to forecast wind power and solar irradiance can enhance accuracy.
Article
Environmental Sciences
Aishwarya Raman, Avelino F. Arellano, Luca Delle Monache, Stefano Alessandrini, Rajesh Kumar
Summary: This study applies an analog-based post-processing method with the WRF-Chem model to improve short range forecasts of AOD. By using historical analog forecasts and a Kalman Filter, the AOD forecast accuracy is enhanced, especially in the western United States. The study also highlights the importance of accurately simulating total precipitable water in the model to ensure the quality of the analogs.
ATMOSPHERIC ENVIRONMENT
(2021)
Article
Green & Sustainable Science & Technology
Francis M. Lopes, Ricardo Conceicao, Hugo G. Silva, Rui Salgado, Manuel Collares-Pereira
Summary: This study enhances operational strategies of concentrating solar power plants by utilizing post-processing methods on numerical weather predictions to improve the accuracy of electricity forecasts. By using short-term forecasts from the Integrated Forecasting System and measurements in southern Portugal, the research significantly increases the prediction accuracy in hourly forecasts.
Article
Geosciences, Multidisciplinary
Saimy Davis, Likhitha Pentakota, Nikita Saptarishy, Pradeep. P. Mujumdar
Summary: This study explores the application of a model coupling framework in predicting urban floods in Bangalore, India. By simulating extreme events using the WRF model and capturing the urban catchment response using the PCSWMM model, successful flood forecasts were made, highlighting the crucial role of high-resolution rainfall forecasts from the WRF model.
FRONTIERS IN EARTH SCIENCE
(2022)
Article
Green & Sustainable Science & Technology
Narjes Azizi, Maryam Yaghoubirad, Meisam Farajollahi, Abolfzl Ahmadi
Summary: This article evaluates the potential of a 20 MW solar photovoltaic power plant in Zahedan city and predicts solar radiation and temperature for the next ten years using MLP, LSTM, GRU, CNN, and CNN-LSTM models with monthly data from 1984 to 2021. The CNN model shows the best performance with four input parameters and two outputs, achieving root mean square error values of 12.68 W/m2 and 1.75 degrees C for global horizontal irradiance and temperature, respectively. Relative humidity has a more significant effect on the model compared to surface pressure. The average annual power output for the period of 2022 to 2031 is predicted to be 50.37 GWh.
Article
Meteorology & Atmospheric Sciences
Yuanyuan Liu
Summary: This article discusses the use of the Kalman filter to improve solar irradiance predictions, and proposes ensemble prediction and measurement to enhance accuracy. Ensemble prediction also provides an estimation of solar irradiance uncertainty, which is useful for energy production forecasts.
ADVANCES IN METEOROLOGY
(2022)
Article
Green & Sustainable Science & Technology
Cyril Voyant, Gilles Notton, Jean-Laurent Duchaud, Luis Antonio Garcia Gutierrez, Jamie M. Bright, Dazhi Yang
Summary: With the increasing share of intermittent renewable energy, advanced solar power forecasting models are needed to optimize the operation of solar power plants. This study compares the performance of advanced models with naive reference methods and considers the benefits of ensemble forecasting. The combination method and ARTU method statistically offer the best results for the proposed study conditions.
Article
Energy & Fuels
John Boland, Sleiman Farah
Summary: Accurately forecasting output of grid connected wind and solar systems is crucial for increasing renewable energy penetration on the electrical network. Statistical forecasting tools were used to generate forecasts with prediction intervals and tested on wind and solar farms in Australia, showing good performance and adaptability for short term forecasting on different time scales.
Article
Energy & Fuels
Armando Castillejo-Cuberos, John Boland, Rodrigo Escobar
Summary: A novel deterministic forecast method was proposed, considering irradiance pattern classification, Markov chains, fuzzy logic and an operational approach, to provide effective and operationally feasible forecasting techniques for better grid integration. Results showed that lead time has the greatest impact on forecasting performance, and the new method outperformed traditional methods in different weather conditions.
Article
Engineering, Civil
Hussain Shahzad, Baden Myers, Guna Hewa, John Boland, Tim Johnson
Summary: This study evaluates the performance of the Storm Water Management Model (SWMM) in representing water sensitive urban design (WSUD) elements in urban catchments. The results show that SWMM can accurately simulate runoff series before and after the installation of leaky wells, with the best performance achieved by using storage nodes to represent leaky wells at the catchment scale.
JOURNAL OF HYDROLOGIC ENGINEERING
(2021)
Article
Energy & Fuels
John Boland, Sleiman Farah, Lei Bai
Summary: Accurately forecasting the output of wind and solar systems is crucial for increasing the penetration of renewable energy on the grid. While there has been extensive research on solar and wind resource forecasting, there is still a lack of focus on system output forecasting, particularly in dealing with the concept of clear sky output and conditionally changing variance.
Article
Construction & Building Technology
Mina Rouhollahi, David Whaley, Josh Byrne, John Boland
Summary: Energy-efficient dwellings play a crucial role in urban energy conservation, and the allocation of residential trees can effectively mitigate the Urban Heat Island (UHI) effect. This study evaluates the impact of different parameters of residential tree planting on the building-surround relationship and proposes an optimal arrangement. The results suggest that deciduous trees save energy bi-seasonally, and the addition of evergreen trees can further enhance energy conservation without negative effects on building thermal response.
ENERGY AND BUILDINGS
(2022)
Article
Engineering, Civil
Hussain Shahzad, Baden Myers, Guna Hewa, Tim Johnson, John Boland, Harsha R. Sadhpare
Summary: This study investigated the impact of storage capacity on the performance of kerbside leaky well systems, using continuous simulation methods and hydrological analysis to determine the potential benefits of distributing stormwater storage volume as a kerbside infiltration system in the catchment area.
JOURNAL OF HYDROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Mawloud Guermoui, Said Benkaciali, Kacem Gairaa, Kada Bouchouicha, Tayeb Boulmaiz, John W. Boland
Summary: A new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, with experimental results showing its superiority in prediction accuracy and outperforming benchmarking models.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Environmental Sciences
Hussain Shahzad, Baden Myers, Guna Hewa, Tim Johnson, John Boland, Hassan Mujtaba
Summary: This article investigates the harmful effects of stormwater discharge on receiving water bodies and focuses on the efficacy of distributed curbside leaky well systems in improving stormwater quality. The results suggest that the limited storage capacity of current systems only leads to minimum reduction in pollutant transport. The study also raises concerns about the potential increase in pollutant concentration in runoff outflows from Australian residential catchments if infill development policies are not followed.
Review
Green & Sustainable Science & Technology
Mina Rouhollahi, David Whaley, Monica Behrend, Josh Byrne, John Boland
Summary: A significant shift towards consolidating residential neighbourhoods has had a dramatic impact on the Australian national urban tree canopy benchmark. Previous tree planting strategies in densely settled residential suburbs are no longer sufficient for environmental and energy conservation goals. A review of original research conducted over the past two decades has led to a better understanding of the relationship between trees and the built environment. This review establishes potential tree allocation parameters for urban energy conservation within the constraints of residential landscapes.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Engineering, Environmental
H. Shahzad, B. Myers, J. Boland, G. Hewa, T. Johnson
Summary: Distributed infiltration systems can significantly reduce runoff flowrate at catchment scale, but their impact on runoff volume reduction is not significant. Curbside infiltration systems can effectively decrease runoff volume for storms with runoff flowrates below 100 L/s.
Article
Chemistry, Analytical
Krishantha Kodithuwakku, Jianyin Huang, Casey L. L. Doolette, Sean Mason, John Boland, Enzo Lombi, Niklas J. J. Lehto, Peter R. R. Teasdale
Summary: The availability of soil nitrogen for plant uptake can be affected by various soil factors, and conventional extraction techniques may affect the measurement of plant-available N concentrations. The diffusive gradients in thin-films (DGT) technique can overcome these limitations and has been successfully used to estimate the plant-available fractions of nutrients in soils. Therefore, evaluating the use of DGT for measuring NO3- and NH4+ in different soils and examining the factors affecting the plant-availability of these ions in soils is important.
ENVIRONMENTAL CHEMISTRY
(2022)
Article
Environmental Sciences
Mark Carey, John Boland, Gunnar Keppel
Summary: The species-area relationship (SAR) is commonly studied in ecology and can be expressed as either a semi-log or power-law relationship. This study introduces a new model that smoothly transitions between the two forms. Applying this model to 100 datasets, it was found that the power-law form was preferred in 68% of comparisons, both forms were supported in 40% of cases, and an intermediate model best explained the data in 44% of cases. This research demonstrates the utility of a simple intermediate SAR model.
ENVIRONMENTAL MODELING & ASSESSMENT
(2023)
Article
Ecology
Mark Carey, John Boland, Gunnar Keppel
Summary: This study compares the performance of models adjusting or substituting for island area with measures of habitat diversity, island age, and resource availability. The results show that weighting island area by habitat diversity and resource availability improves statistical significance and model fits, while weighting by island age does not. Therefore, it is recommended to consider climate, topography, and geology when studying biodiversity patterns on islands, rather than relying solely on island area as a proxy.
JOURNAL OF BIOGEOGRAPHY
(2023)
Article
Water Resources
Tesfa Gebrie Andualem, Stefan Peters, Guna A. Hewa, John Boland, Baden R. Myers
Summary: Urbanization and changes in land use have significant impacts on urban catchments. This study used high spatial resolution imagery to examine the changes in land use and land cover (LULC) in the highly urbanized Dry Creek catchment in Adelaide, South Australia. The findings showed that urban development has led to an increase in built-up areas and a decline in grass cover. Moreover, urbanization has intensified impervious area coverage, affecting surface runoff.
APPLIED WATER SCIENCE
(2023)
Review
Environmental Studies
Tesfa Gebrie Andualem, Guna A. Hewa, Baden R. Myers, Stefan Peters, John Boland
Summary: Soil erosion and sediment transport have significant consequences for agriculture, water quality, and stream channels. Understanding these processes and their interactions is crucial for assessing environmental impacts. This review aims to identify a suitable model for catchment-scale soil erosion and sediment transport. The study considers various model selection processes and acknowledges the limitations and uncertainties associated with these models.
Article
Energy & Fuels
Siddharth Sradhasagar, Omkar Subhasish Khuntia, Srikanta Biswal, Sougat Purohit, Amritendu Roy
Summary: In this study, machine learning models were developed to predict the bandgap and its character of double perovskite materials, with LGBMRegressor and XGBClassifier models identified as the best predictors. These models were further employed to predict the bandgap of novel bismuth-based transition metal oxide double perovskites, showing high accuracy, especially in the range of 1.2-1.8 eV.
Article
Energy & Fuels
Wei Shuai, Haoran Xu, Baoyang Luo, Yihui Huang, Dong Chen, Peiwang Zhu, Gang Xiao
Summary: In this study, a hybrid model based on numerical simulation and deep learning is proposed for the optimization and operation of solar receivers. By applying the model to different application scenarios and considering multiple performance objectives, small errors are achieved and optimal structure parameters and heliostat scales are identified. This approach is not only applicable to gas turbines but also heating systems.
Article
Energy & Fuels
Mubashar Ali, Zunaira Bibi, M. W. Younis, Muhammad Mubashir, Muqaddas Iqbal, Muhammad Usman Ali, Muhammad Asif Iqbal
Summary: This study investigates the structural, mechanical, and optoelectronic properties of the BaCuF3 fluoroperovskite using the first-principles modelling approach. The stability and characteristics of different cubic structures of BaCuF3 are evaluated, and the alpha-BaCuF3 and beta-BaCuF3 compounds are found to be mechanically stable with favorable optical properties for solar cells and high-frequency UV applications.
Article
Energy & Fuels
Dong Le Khac, Shahariar Chowdhury, Asmaa Soheil Najm, Montri Luengchavanon, Araa mebdir Holi, Mohammad Shah Jamal, Chin Hua Chia, Kuaanan Techato, Vidhya Selvanathan
Summary: A novel recycling system is proposed in this study to decompose and reclaim the constituent materials of organic-inorganic perovskite solar cells (PSCs). By utilizing a one-step solution process extraction approach, the chemical composition of each layer is successfully preserved, enabling their potential reuse. The proposed recycling technique helps mitigate pollution risks, minimize waste generation, and reduce recycling costs.
Article
Energy & Fuels
Peijie Lin, Feng Guo, Xiaoyang Lu, Qianying Zheng, Shuying Cheng, Yaohai Lin, Zhicong Chen, Lijun Wu, Zhuang Qian
Summary: This paper proposes an open-set fault diagnosis model for PV arrays based on 1D VoVNet-SVDD. The model accurately diagnoses various types of faults and is capable of identifying unknown fault types.
