Article
Green & Sustainable Science & Technology
Ravinesh C. Deo, A. A. Masrur Ahmed, David Casillas-Perez, S. Ali Pourmousavi, Gary Segal, Yanshan Yu, Sancho Salcedo-Sanz
Summary: Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models can aid renewable energy engineers in monitoring and forecasting solar photovoltaic power generation. A major challenge is the systematic bias in TCDC simulations induced by errors in the numerical model parameterization stages. Correction of GFS-derived cloud forecasts at multiple time steps can improve energy forecasts in electricity grids to bring better grid stability or certainty in the supply of solar energy.
Article
Green & Sustainable Science & Technology
Vateanui Sansine, Pascal Ortega, Daniel Hissel, Marania Hopuare
Summary: Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. In this study, a particle swarm optimization algorithm combined with three stand-alone models was used for solar irradiance prediction, and compared with other stand-alone models. The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting.
Article
Chemistry, Multidisciplinary
Caston Sigauke, Edina Chandiwana, Alphonce Bere
Summary: Accurate forecasting of global horizontal irradiance (GHI) is crucial for power grid stability. This research proposes the use of spatial regression coupled with Gaussian Process Regression (GP Spatial) and the GP Autoregressive Spatial model (GP-AR Spatial) for GHI prediction. The results show that the GP model outperforms the benchmark model in terms of accuracy.
APPLIED SCIENCES-BASEL
(2023)
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
Multidisciplinary Sciences
Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Judah Cohen, Miruna Oprescu, Ernest Fraenkel, Lester Mackey
Summary: Subseasonal forecasting is crucial for water allocation, wildfire management, and disaster mitigation. However, current models suffer from errors in representing atmospheric dynamics and physics. To address this, we introduce an adaptive bias correction method that combines dynamical forecasts with observations using machine learning, leading to significant improvements in temperature and precipitation prediction skill.
NATURE COMMUNICATIONS
(2023)
Article
Energy & Fuels
Takeshi Watanabe, Hideaki Takenaka, Daisuke Nohara
Summary: A correction method for numerical weather prediction model data is developed in this study, using surface GHI information and satellite observations to improve forecast accuracy. The correction effects have seasonal and regional characteristics, with the method generally improving forecast quality from spring to autumn in Japan.
Article
Chemistry, Multidisciplinary
Jiri Pospichal, Martin Kubovcik, Iveta Dirgova Luptakova
Summary: This paper extends the attention mechanism of the Transformer deep neural network model and combines spatiotemporal properties in solar irradiance prediction. The predicted results are included in the input data and achieve better results than competing methods.
APPLIED SCIENCES-BASEL
(2022)
Article
Energy & Fuels
Martin Janos Mayer, Dazhi Yang, Balazs Szintai
Summary: This study thoroughly verifies the irradiance and PV power forecasts from European Centre for Medium-Range Weather Forecasts and Meteo-France's AROME models using ground-based measurements from research-grade radiometry stations and actual PV plants in Hungary. The results show that the performance of irradiance forecasts can only be used to infer the performance of PV power forecasts to a certain extent.
Article
Environmental Sciences
Soumya Das, Marc G. Genton, Yasser M. Alshehri, Georgiy L. Stenchikov
Summary: As part of Saudi Vision 2030, Saudi Arabia aims to reduce its dependency on oil and promote renewable energy. This article proposes a model for short-term point forecast and simulation of solar irradiance, taking into account the strong dependency of GHIs on aerosol optical depths and the periodic correlation structure of GHIs.
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
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
Environmental Sciences
Hao Yang, Long Wang, Chao Huang, Xiong Luo
Summary: A novel feature extracting method using a three-dimensional CNN is proposed for GHI forecasting, achieving a minimum average RMSE of 62 W/m2 with a 15.2% improvement in Skill score compared to the baseline method. Multiple machine learning algorithms are introduced to explore forecasting accuracy with different input features on a large dataset.
Article
Green & Sustainable Science & Technology
Priya Gupta, Rhythm Singh
Summary: This study proposes a hybrid MEMD-PCA-GRU model for accurate and reliable global horizontal irradiance (GHI) forecasting. The model utilizes multivariate empirical mode decomposition (MEMD) to remove non-stationary and nonlinear deficiencies within target series and meteorological predictors. Principal component analysis (PCA) is then applied to identify informative features, and the gated recurrent unit (GRU) is utilized for GHI prediction. The proposed model outperforms other hybrid and standalone models, showing stable and good performance across different climatic conditions.
Article
Energy & Fuels
Latifa El Boujdaini, Ahmed Mezrhab, Mohammed Amine Moussaoui
Summary: This study predicts the direct normal and global horizontal irradiances using the Artificial Neural Network method with new calculated input variables for more accurate daily and monthly forecasts. The cloudy sky index shows good accuracy in estimating GHI and DNI, with the best performance observed at the monthly time scale.
ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
(2021)
Article
Meteorology & Atmospheric Sciences
S. K. Mukkavilli, A. A. Prasad, R. A. Taylor, J. Huang, R. M. Mitchell, A. Troccoli, M. J. Kay
ATMOSPHERIC RESEARCH
(2019)
Article
Meteorology & Atmospheric Sciences
Sarah Chapman, Marcus Thatcher, Alvaro Salazar, James E. M. Watson, Clive A. McAlpine
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2019)
Article
Meteorology & Atmospheric Sciences
S. Sharmila, K. J. E. Walsh, M. Thatcher, S. Wales, S. Utembe
JOURNAL OF CLIMATE
(2020)
Article
Meteorology & Atmospheric Sciences
K. J. E. Walsh, S. Sharmila, M. Thatcher, S. Wales, S. Utembe, A. Vaughan
JOURNAL OF CLIMATE
(2020)
Article
Meteorology & Atmospheric Sciences
Andreas Schiller, Gary B. Brassington, Peter Oke, Madeleine Cahill, Prasanth Divakaran, Mikhail Entel, Justin Freeman, David Griffin, Mike Herzfeld, Ron Hoeke, Xinmei Huang, Emlyn Jones, Edward King, Barbra Parker, Tracey Pitman, Uwe Rosebrock, Jessica Sweeney, Andy Taylor, Marcus Thatcher, Robert Woodham, Aihong Zhong
JOURNAL OF OPERATIONAL OCEANOGRAPHY
(2020)
Article
Environmental Sciences
Mathew J. Lipson, Marcus Thatcher, Melissa A. Hart, Andrew Pitman
ENVIRONMENTAL RESEARCH LETTERS
(2019)
Article
Geography, Physical
Francois A. Engelbrecht, Curtis W. Marean, Richard M. Cowling, Christien J. Engelbrecht, Frank H. Neumann, Louis Scott, Ramapulana Nkoana, David O'Neal, Erich Fisher, Eric Shook, Janet Franklin, Marcus Thatcher, John L. McGregor, Jacobus Van der Merwe, Zane Dedekind, Mark Difford
QUATERNARY SCIENCE REVIEWS
(2019)
Article
Meteorology & Atmospheric Sciences
Giovanni Di Virgilio, Jason P. Evans, Alejandro Di Luca, Michael R. Grose, Vanessa Round, Marcus Thatcher
Article
Meteorology & Atmospheric Sciences
Asmerom F. Beraki, Yushi Morioka, Francois A. Engelbrecht, Masami Nonaka, Marcus Thatcher, Nomkwezane Kobo, Swadhin Behera
Article
Environmental Sciences
Sarah Chapman, Jozef Syktus, Ralph Trancoso, Alvaro Salazar, Marcus Thatcher, James E. M. Watson, Erik Meijaard, Douglas Sheil, Paul Dargusch, Clive A. McAlpine
ENVIRONMENTAL RESEARCH LETTERS
(2020)
Article
Meteorology & Atmospheric Sciences
Jack Katzfey, Heinke Schluezen, Peter Hoffmann, Marcus Thatcher
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2020)
Article
Green & Sustainable Science & Technology
Jing Huang, Ben Jones, Marcus Thatcher, Judith Landsberg
JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY
(2020)
Article
Multidisciplinary Sciences
Kathryn M. Emmerson, Jeremy D. Silver, Marcus Thatcher, Alan Wain, Penelope J. Jones, Andrew Dowdy, Edward J. Newbigin, Beau W. Picking, Jason Choi, Elizabeth Ebert, Tony Bannister
Summary: The most severe thunderstorm asthma event occurred in Melbourne, Australia in 2016 was thought to be caused by pollen rupture in high humidity conditions releasing sub-pollen particles easily inhaled into the lungs. However, the existing humidity hypothesis could not explain the event, leading to the consideration of other mechanisms for pollen rupturing.
Article
Construction & Building Technology
Prabhasri Herath, Marcus Thatcher, Huidong Jin, Xuemei Bai
Summary: This study evaluates different urban surface parameters (USPs) to assess their effectiveness in mitigating urban heat. Results show that roofs with high albedos are the best daytime heat mitigation strategy, while green roofs are most effective at night. Vegetation ratio, green and cool roofs show near-linear negative relationships with heat, and trees are more effective when distributed in both canyon and urban parks.
SUSTAINABLE CITIES AND SOCIETY
(2021)
Article
Environmental Sciences
Yi Qin, Andrew D. L. Steven, Thomas Schroeder, Tim R. McVicar, Jing Huang, Martin Cope, Shangzhi Zhou
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2019)
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.
