Article
Energy & Fuels
Yanhong Ma, Qingquan Lv, Ruixiao Zhang, Yanqi Zhang, Honglu Zhu, Wansi Yin
Summary: Accurate PV power forecasting is crucial for safe and stable operation of PV power plants and reasonable grid dispatching. This research proposes a method based on irradiance correction and error forecasting to improve short-term forecasting accuracy, showing significant reductions in RMSE through NWP correction, model optimization, and error correction.
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
Engineering, Multidisciplinary
Andres Gersnoviez, Juan C. Gamez-Granados, Marta Cabrera-Fernandez, Isabel Santiago, Eduardo Canete-Carmona, Maria Brox
Summary: This paper proposes a classifier that uses fuzzy logic to classify daily irradiance profiles. The article combines data mining and supervised learning algorithms to obtain an initial system and then simplifies it using fuzzy classifiers and fuzzy tabular simplification techniques. The obtained classifier effectively handles the ambiguity in daily irradiance profiles. A neuro-fuzzy system is also designed to predict the performance of the photovoltaic installation, considering various factors such as weather type, ambient temperature, and installation degradation.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Green & Sustainable Science & Technology
Martin Janos Mayer, Dazhi Yang
Summary: This paper extends the model-chain-based solar forecasting framework to the probability space by using a calibrated ensemble of model chains to generate probabilistic PV power forecasts. Empirical results show that adequate post-processing can improve the calibration of the forecasts.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
E. G. D. Barros, B. B. Van Aken, A. R. Burgers, L. H. Slooff-Hoek, R. M. Fonseca
Summary: This study introduces a multi-objective optimization framework to develop smart solar park configurations under uncertainty, taking into account multifunctional land use. By combining a solar park simulator and techno-economic model to evaluate key performance indicators, uncertainties related to meteorological aspects throughout the park lifetime are characterized using a set of scenarios based on historical data variability.
Article
Energy & Fuels
Seyedsoroush Sadatifar, Eric Johlin
Summary: This study explores a design framework for optimizing the configuration of BIPV shading devices, considering the combined benefits of power generation, daylighting quality, and radiative heating and cooling loads. The findings highlight the significant influences of location, building geometry, and user preferences on the value of the system.
Article
Computer Science, Information Systems
Elissaios Alexios Papatheofanous, Vasileios Kalekis, Georgios Venitourakis, Filippos Tziolos, Dionysios Reisis
Summary: PV power production is highly variable due to meteorological effects. Researchers propose using computer vision and deep learning to forecast short-term irradiance using sky images. A method based on sun localization is introduced to improve the accuracy of irradiance estimation and the integration of models on an FPGA enables real-time control of PV production.
Article
Energy & Fuels
Ewa Chodakowska, Joanicjusz Nazarko, Lukasz Nazarko, Hesham S. S. Rabayah, Raed M. M. Abendeh, Rami Alawneh
Summary: This study applies ARIMA models to predict seasonal solar radiation in different climatic conditions, evaluates the performance and prediction capacity of the models, and develops solar radiation forecasting models using data from Jordan and Poland. The research findings demonstrate that ARIMA models are suitable for solar radiation forecasting and can support the stable long-term integration of renewable energy.
Article
Thermodynamics
Xiaoqiao Huang, Jun Liu, Shaozhen Xu, Chengli Li, Qiong Li, Yonghang Tai
Summary: Due to the intermittent and fluctuation of solar energy, photovoltaic (PV) power forecasting, including solar irradiance forecasting, is necessary for the grid connection of photovoltaic power stations. To address the challenges, a novel ultra-short-term solar irradiance forecasting method based on a 3D ConvLSTM-CNN hybrid model is proposed in this paper. The method shows promising performance and achieves significant improvements over the persistence model for 5-min ahead global horizontal irradiance (GHI) prediction.
Article
Computer Science, Artificial Intelligence
Jianzhou Wang, Yuyang Gao
Summary: This study aims to establish an integrated interval forecasting system for solar radiation, using feature extraction and a hybrid kernel relevance vector machine. The proposed system achieves higher coverage rate and narrower interval width in solar radiation forecasting.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Engineering, Chemical
Nur Liyana Mohd Jailani, Jeeva Kumaran Dhanasegaran, Gamal Alkawsi, Ammar Ahmed Alkahtani, Chen Chai Phing, Yahia Baashar, Luiz Fernando Capretz, Ali Q. Al-Shetwi, Sieh Kiong Tiong
Summary: Solar energy is a significant renewable energy source that can meet global energy needs while reducing global warming. Accurate forecasting of renewable energy output is crucial for grid reliability and sustainability, as well as reducing risk and expense in energy markets and systems.
Article
Energy & Fuels
Jianzhou Wang, Yilin Zhou, Zhiwu Li
Summary: This article presents a comprehensive system for solar photovoltaic power forecasting, which enhances the utility and stability of the predictive system through automatic optimization and multi-objective intelligent optimization algorithms. The study demonstrates that the designed system achieves high accuracy and stability in predicting photovoltaic power data.
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
Computer Science, Artificial Intelligence
Jianzhou Wang, Yilin Zhou, He Jiang
Summary: With the continuous increase in global photovoltaic installations, it has become essential to accurately forecast and manage photovoltaic power generation. This paper proposes a novel hybrid interval prediction system that combines various techniques to improve the stability and accuracy of photovoltaic prediction. The system is verified to have high prediction efficiency.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Energy & Fuels
B. L. Madhavan, M. Venkat Ratnam
Summary: The study found that during the annular solar eclipse, there were significant changes in spectral and broadband irradiances, with diffuse fraction showing spectral dependence while deviations in global horizontal irradiance and direct normal irradiance were comparable at different wavelengths. Furthermore, there were varying reductions in broadband irradiances at different wavelengths during the eclipse, with PV energy generation being reduced by 37% compared to a clear-sky day.
Article
Energy & Fuels
Martin Janos Mayer, Gyula Grof
Article
Energy & Fuels
Martin Janos Mayer, Artur Szilagyi, Gyula Grof
Article
Green & Sustainable Science & Technology
Martin Janos Mayer, Artur Szilagyi, Gyula Grof
Summary: This paper introduces a methodology to calculate the product environmental footprint and the levelized cost of electricity for ground-mounted, grid-connected PV power plants. The study shows that environmental impacts can be reduced by adjusting design parameters, although not all impacts can be reduced simultaneously. By accepting a small cost increase, a majority of potential impact reduction can be achieved.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Energy & Fuels
Martin Janos Mayer, Gyula Grof
Summary: Physical modeling plays a crucial role in forecasting the power production of grid-connected PV power plants. Different model chains can lead to significant differences in forecast accuracy, with irradiance separation and transposition modeling identified as the most critical calculation steps. Wind speed forecasts have only a marginal effect on PV power prediction accuracy.
Article
Energy & Fuels
Nora Varga, Martin Janos Mayer
Summary: This paper presents a detailed method for calculating irradiance distribution of shading between rows in ground-mounted PV plants with multiple parallel mounting structure, and demonstrates the impact of the treatment of the film on PV rows. The study suggests that modules at the same height should be connected to the same string to minimize shading losses.
Article
Thermodynamics
Martin Janos Mayer
Summary: The accuracy of ground-mounted photovoltaic plant simulation and optimization is dependent on the reliability of meteorological datasets, which are influenced by their source, length, and resolution. Quantifying the impact of irradiance data temporal resolution on design parameters and profitability is crucial for enhancing the credibility of photovoltaic design simulations. Aggregating datasets by sampling can provide more reliable results even at lower resolutions, making it an effective technique for accurate photovoltaic optimization without the long calculation time of minute-resolution simulations.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Energy & Fuels
Martin Janos Mayer
Summary: Accurate PV power forecasts are crucial for grid integration. Knowing critical parameters like nameplate capacities and module orientation can significantly reduce prediction errors in physical model chains used for power forecast calculations.
Article
Green & Sustainable Science & Technology
David Markovics, Martin Janos Mayer
Summary: This study compares 24 machine learning models for deterministic day-ahead power forecasting and finds that kernel ridge regression and multilayer perceptron are the most accurate models. Supplementary inputs like Sun position angles and irradiance values can significantly reduce the prediction error. Hyperparameter tuning is essential for optimizing the models' performance.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Green & Sustainable Science & Technology
Martin Janos Mayer
Summary: This paper proposes a hybrid method combining physical and machine learning approaches for irradiance-to-power conversion in photovoltaic power forecasting. The study compares the performance of physical, data-driven, and hybrid methods for power forecasting of PV plants in Hungary. Results show that the hybrid method with physically calculated predictors significantly reduces errors compared to optimized physical models and machine learning without physical considerations. The separation and transposition modeling steps are found to be the most important in physical modeling. Optimization of physical model chains is important even in hybrid modeling. The guidelines and recommendations provided in this paper can help improve the accuracy of PV power forecasts.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Green & Sustainable Science & Technology
Martin Janos Mayer, Dazhi Yang
Summary: This paper extends the model-chain-based solar forecasting framework to the probability space by using a calibrated ensemble of model chains to generate probabilistic PV power forecasts. Empirical results show that adequate post-processing can improve the calibration of the forecasts.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2022)
Article
Energy & Fuels
Martin Janos Mayer, Bence Biro, Botond Szuecs, Attila Aszodi
Summary: This paper proposes a new method based on artificial neural networks to model the relationship between weather data and renewable energy generation. The method generates synthetic power production and load profiles for future years and ensures the reliability of the simulation through a novel variance-correction method. The research shows that weather-dependent renewable generation can cover up to 60% of the annual power consumption in Hungary, but higher carbon-free electricity share targets require a combination of nuclear power and renewable sources.
Article
Green & Sustainable Science & Technology
Martin Janos Mayer, Dazhi Yang
Summary: This study investigates the uncertainty in photovoltaic (PV) power forecasting by using ensemble numerical weather prediction (NWP) and ensemble model chain methods. It is demonstrated that the best probabilistic PV power forecast needs to consider both ensemble NWP and ensemble model chain. Furthermore, the point forecast accuracy is significantly improved through this pairing strategy. The recommended strategy achieves a mean-normalized continuous ranked probability score of 18.4% and a root mean square error of 42.1%.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Proceedings Paper
Energy & Fuels
M. J. Mayer, V. Nyerges, A. Schroth
2015 5TH INTERNATIONAL YOUTH CONFERENCE ON ENERGY (IYCE)
(2015)
Proceedings Paper
Energy & Fuels
Martin Janos Mayer
2015 5TH INTERNATIONAL YOUTH CONFERENCE ON ENERGY (IYCE)
(2015)
Article
Energy & Fuels
Shitong Fang, Houfan Du, Tao Yan, Keyu Chen, Zhiyuan Li, Xiaoqing Ma, Zhihui Lai, Shengxi Zhou
Summary: This paper proposes a new type of nonlinear VIV energy harvester (ANVEH) that compensates for the decrease in peak energy output at low wind speeds by introducing an auxiliary structure. Theoretical and experimental results show that ANVEH performs better than traditional nonlinear VIV energy harvesters under various system parameter variations.
Article
Energy & Fuels
Wei Jiang, Shuo Zhang, Teng Wang, Yufei Zhang, Aimin Sha, Jingjing Xiao, Dongdong Yuan
Summary: A standardized method was developed to evaluate the availability of solar energy resources in road areas, which combined the Analytic Hierarchy Process (AHP) and the Geographic Information System (GIS). By analyzing critical factors and using a multi-indicator evaluation method, the method accurately evaluated the utilization of solar energy resources and guided the optimal location selection for road photovoltaic (PV) projects. The results provided guidance for the application of road PV projects and site selection for route corridors worldwide, promoting the integration of transportation and energy.
Article
Energy & Fuels
Chang Liu, Jacob A. Wrubel, Elliot Padgett, Guido Bender
Summary: The study investigates the effects of coating defects on the performance of the anode porous transport layer (PTL) in water electrolyzers. The results show that an increasing fraction of uncoated regions on the PTL leads to decreased cell performance, with continuous uncoated regions having a more severe impact compared to multiple thin uncoated strips.
Article
Energy & Fuels
Marcos Tostado-Veliz, Xiaolong Jin, Rohit Bhakar, Francisco Jurado
Summary: In this paper, a coordinated charging price mechanism for clusters of parking lots is proposed. The research shows that enabling vehicle-to-grid characteristics can bring significant economic benefits for users and the cluster coordinator, and vehicle-to-grid impacts noticeably on the risk-averse character of the uncertainty-aware strategies. The developed pricing mechanism can reduce the cost for users, avoiding to directly translate the energy cost to charging points.
Article
Energy & Fuels
Duan Kang
Summary: Building an energy superpower is a key strategy for China and a long-term goal for other countries. This study proposes an evaluation system and index for measuring energy superpower, and finds that China has significantly improved its ranking over the past 21 years, surpassing other countries.
Article
Energy & Fuels
Fucheng Deng, Yifei Wang, Xiaosen Li, Gang Li, Yi Wang, Bin Huang
Summary: This study investigated the synergistic blockage mechanism of sand and hydrate in gravel filling layer and the evolution of permeability in the layer. Experimental models and modified permeability models were established to analyze the effects of sand particles and hydrate formation on permeability. The study provided valuable insights for the safe and efficient exploitation of hydrate reservoirs.
Article
Energy & Fuels
Hao Wang, Xiwen Chen, Natan Vital, Edward Duffy, Abolfazl Razi
Summary: This study proposes a HVAC energy optimization model based on deep reinforcement learning algorithm. It achieves 37% energy savings and ensures thermal comfort for open office buildings. The model has a low complexity, uses a few controllable factors, and has a short training time with good generalizability.
Article
Energy & Fuels
Moyue Cong, Yongzhuo Gao, Weidong Wang, Long He, Xiwang Mao, Yi Long, Wei Dong
Summary: This study introduces a multi-strategy ultra-wideband energy harvesting device that achieves high power output without the need for external power input. By utilizing asymmetry, stagger array, magnetic coupling, and nonlinearity strategies, the device maintains a stable output voltage and high power density output at non-resonant frequencies. Temperature and humidity monitoring are performed using Bluetooth sensors to adaptively assess the device.
Article
Energy & Fuels
Tianshu Dong, Xiudong Duan, Yuanyuan Huang, Danji Huang, Yingdong Luo, Ziyu Liu, Xiaomeng Ai, Jiakun Fang, Chaolong Song
Summary: Electrochemical water splitting is crucial for hydrogen production, and improving the hydrogen separation rate from the electrode is essential for enhancing water electrolyzer performance. However, issues such as air bubble adhesion to the electrode plate hinder the process. Therefore, a methodology to investigate the two-phase flow within the electrolyzer is in high demand. This study proposes using a microfluidic system as a simulator for the electrolyzer and optimizing the two-phase flow by manipulating the micro-structure of the flow.
Article
Energy & Fuels
Shuo Han, Yifan Yuan, Mengjiao He, Ziwen Zhao, Beibei Xu, Diyi Chen, Jakub Jurasz
Summary: Giving full play to the flexibility of hydropower and integrating more variable renewable energy is of great significance for accelerating the transformation of China's power energy system. This study proposes a novel day-ahead scheduling model that considers the flexibility limited by irregular vibration zones (VZs) and the probability of flexibility shortage in a hydropower-variable renewable energy hybrid generation system. The model is applied to a real hydropower station and effectively improves the flexibility supply capacity of hydropower, especially during heavy load demand in flood season.
Article
Energy & Fuels
Zhen Wang, Kangqi Fan, Shizhong Zhao, Shuxin Wu, Xuan Zhang, Kangjia Zhai, Zhiqi Li, Hua He
Summary: This study developed a high-performance rotary energy harvester (AI-REH) inspired by archery, which efficiently accumulates and releases ultralow-frequency vibration energy. By utilizing a magnetic coupling strategy and an accumulator spring, the AI-REH achieves significantly accelerated rotor speeds and enhanced electric outputs.
Article
Energy & Fuels
Yi Yang, Qianyi Xing, Kang Wang, Caihong Li, Jianzhou Wang, Xiaojia Huang
Summary: In this study, a novel hybrid Quantile Regression (QR) model is proposed for Probabilistic Load Forecasting (PLF). The model integrates causal dilated convolution, residual connection, and Bidirectional Long Short-Term Memory (BiLSTM) for multi-scale feature extraction. In addition, a Combined Probabilistic Load Forecasting System (CPLFS) is proposed to overcome the inherent flaws of relying on a single model. Simulation results show that the hybrid QR outperforms traditional models and CPLFS exceeds the best benchmarks in terms of prediction accuracy and stability.
Article
Energy & Fuels
Wen-Jiang Zou, Young-Bae Kim, Seunghun Jung
Summary: This paper proposes a dynamic prediction model for capacity fade in vanadium redox flow batteries (VRFBs). The model accurately predicts changes in electrolyte volume and capacity fade, enhancing the competitiveness of VRFBs in energy storage applications.
Article
Energy & Fuels
Yuechao Ma, Shengtie Wang, Guangchen Liu, Guizhen Tian, Jianwei Zhang, Ruiming Liu
Summary: This paper focuses on the balance of state of charge (SOC) among multiple battery energy storage units (MBESUs) and bus voltage balance in an islanded bipolar DC microgrid. A SOC automatic balancing strategy is proposed considering the energy flow relationship and utilizing the adaptive virtual resistance algorithm. The simulation results demonstrate the effectiveness of the proposed strategy in achieving SOC balancing and decreasing bus voltage unbalance.
Article
Energy & Fuels
Raad Z. Homod, Basil Sh. Munahi, Hayder Ibrahim Mohammed, Musatafa Abbas Abbood Albadr, Aissa Abderrahmane, Jasim M. Mahdi, Mohamed Bechir Ben Hamida, Bilal Naji Alhasnawi, A. S. Albahri, Hussein Togun, Umar F. Alqsair, Zaher Mundher Yaseen
Summary: In this study, the control problem of the multiple-boiler system (MBS) is formulated as a dynamic Markov decision process and a deep clustering reinforcement learning approach is applied to obtain the optimal control policy. The proposed strategy, based on bang-bang action, shows superior response and achieves more than 32% energy saving compared to conventional fixed parameter controllers under dynamic indoor/outdoor actual conditions.