Article
Agronomy
Muhammad Tousif Bhatti, Arif A. Anwar
Summary: This paper presents a case study of developing a decision support service for farmers in Pakistan based on rainfall forecast data. The study analyzes the statistical verification of 16-day rainfall forecast data and explores the process of developing such a service using global weather forecast data. The findings show variations in the quality of the forecast across different stations.
AGRICULTURAL AND FOREST METEOROLOGY
(2022)
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
Engineering, Marine
Mengning Wu, Zhen Gao
Summary: The concept of the alpha-factor, a correction factor on the significant wave height limit, considers the effect of weather forecast uncertainty on marine operations. A new response-based correction factor, alpha R, is proposed to quantify the effect of forecast uncertainty on dynamic response of offshore structures. By using response-based criteria, sea state assessment for marine operations can incorporate weather forecast uncertainty, providing efficient decision-making guidance in execution phase.
Article
Energy & Fuels
Dazhi Yang, Wenting Wang, Jamie M. Bright, Cyril Voyant, Gilles Notton, Gang Zhang, Chao Lyu
Summary: Forecasting global horizontal irradiance up to 12 hours ahead is crucial for solar photovoltaics grid integration. In this study, the ECMWF's HRES model and two NOAA models, namely RAP and HRRR, are validated and compared. Results show that HRES forecasts outperform HRRR and RAP forecasts in terms of accuracy.
Article
Chemistry, Analytical
Rongnian Tang, Yuke Ning, Chuang Li, Wen Feng, Youlong Chen, Xiaofeng Xie
Summary: Achieving high-performance numerical weather prediction is crucial for livelihoods and socioeconomic development. A novel spatial LightGBM model is proposed to correct the numerical forecast results at each observation station, incorporating local spatial information and utilizing a specific strategy for high-performance correction.
Article
Engineering, Marine
Zhizheng Wu, Shengzheng Wang, Qiumeng Yuan, Naiyuan Lou, Siyuan Qiu, Li Bo, Xiuzhi Chen
Summary: This paper introduces a transformer-based time series model to recover spatio-temporal discrete weather forecast data and improve voyage optimization accuracy. A convolutional neural network is used to extract multi-dimensional spatial features, which are then input into a transformer attention mechanism to analyze the time characteristics of the weather data. The effectiveness and accuracy of the reconstructed weather forecast data were confirmed experimentally, and the proposed method demonstrated its usefulness in route design and optimization.
Article
Astronomy & Astrophysics
A. Brunet, N. Dahmen, C. Katsavrias, O. Santolik, G. Bernoux, V. Pierrard, E. Botek, F. Darrouzet, A. Nasi, S. Aminalragia-Giamini, C. Papadimitriou, S. Bourdarie, I. A. Daglis
Summary: The H2020 SafeSpace project aims to implement a space weather safety prototype, with a particular focus on predicting deep charging hazards. This paper presents the inner magnetosphere section of the SafeSpace pipeline, which relies on solar wind-driven and hourly updated models to describe the trapped electron environment and the physical processes they undergo. The forecasting performance of this new modeling pipeline was compared to a reference model during the St. Patrick's Day storm in 2015, showing that the new SafeSpace implementation has closer results to observations and a better forecast within the prediction horizon.
SPACE WEATHER-THE INTERNATIONAL JOURNAL OF RESEARCH AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Eva Lucas Segarra, German Ramos Ruiz, Carlos Fernandez Bandera
Summary: Accurate load forecasting in buildings is crucial for various stakeholders, and probabilistic load forecasting (PLF) is essential to manage energy-saving potential. This research introduces a methodology to optimize PLF results by characterizing load forecasts daily and using a calibrated white-box model and real weather forecast. A real case study demonstrates that with this daily characterization, the accuracy of probabilistic load forecasting can be optimized, reaching close to 100% in some cases.
Article
Environmental Sciences
Xueliang Zhao, Qilong Sun, Wanru Tang, Shuang Yu, Boyu Wang
Summary: Wind speed forecasting is critical in various fields, but conventional methods lack accuracy and need post-processing. This paper applies deep learning algorithms for error correction in wind speed prediction, compared with time-series prediction methods. Experimental results show that deep learning methods can improve accuracy without modeling.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Meteorology & Atmospheric Sciences
Chris J. Short, Jon Petch
Summary: This article presents a method for reducing the spin-up of regional NWP models, without data assimilation. By periodically inserting large-scale information from global model analyses into a continuously cycling regional model, the method updates the large scales while preserving fine-scale structures. The study shows that warm-starting significantly reduces the spin-up of precipitation and improves precipitation forecast skill.
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
(2022)
Article
Computer Science, Interdisciplinary Applications
Klaus Altendorfer, Thomas Felberbauer
Summary: A general demand model for the supplier is developed based on observed customer forecasting behaviours. The model considers biased and unbiased information updates as well as demand shifting behaviour. The paper also introduces a correction method for reducing the negative effects of biased demand forecasts and evaluates its performance.
SIMULATION MODELLING PRACTICE AND THEORY
(2023)
Article
Computer Science, Information Systems
Jihoo Son, Jiwon Cha, Hyunsu Kim, Young-Min Wi
Summary: This study proposes a framework for short-term load forecasting (STLF) for holidays, taking into account factors such as calendar, trend, weather, and behind-the-meter photovoltaic (BTM PV) resources. The framework compares the differences between historical holidays and target holidays, quantifies their impact on load differences, and generates modified load profiles. The accuracy of the proposed framework was found to be better than conventional methods.
Article
Environmental Sciences
Haoliang Wang, Shuangqi Yuan, Yubao Liu, Yang Li
Summary: This study evaluates and compares the performance of radar reflectivity and lightning data assimilation in short-term precipitation and lightning forecasts. Both assimilation methods improved the accuracy of forecasts, with radar reflectivity assimilation performing better for precipitation and lightning data assimilation performing better for lightning forecasts, especially in the analysis period and 1-hour forecast.
Article
Green & Sustainable Science & Technology
Christina Brester, Viivi Kallio-Myers, Anders Lindfors, Mikko Kolehmainen, Harri Niska
Summary: The effective integration of solar PV output into overall energy consumption planning and control depends on accurate PV forecasting. However, the availability of numerical weather prediction (NWP) data poses a challenge in training data-driven PV forecasting models. In this study, an artificial neural network (ANN) is trained on weather observations and tested on NWP data, showing better performance than a physical model.
Article
Thermodynamics
Lingwei Zheng, Ran Su, Xinyu Sun, Siqi Guo
Summary: This paper proposes a framework for reverse determination of weather types from historical PV output data, using symbol-sequence histograms to describe PV output volatility and partitionally clustering and proposing a classification rule for weather types. A prediction method combining phase-space reconstruction with an extremely learning machine based single-layer forward net is developed to predict the symbol-sequence histograms. Experimental results show that, compared with weather information from a weather-service supplier, the PV-output prediction errors are significantly reduced by 15.55% (MAPE) and 12.69% (rRMSE).
Article
Thermodynamics
Pengcheng Zhao, Jingang Wang, Liming Sun, Yun Li, Haiting Xia, Wei He
Summary: The production of green hydrogen through water electrolysis is crucial for renewable energy utilization and decarbonization. This research explores the optimal electrode configuration and system design of compactly-assembled industrial electrolyzer. The findings provide valuable insights for industrial application of water electrolysis equipment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
V. Baiju, P. Abhishek, S. Harikrishnan
Summary: Thermally driven adsorption desalination systems (ADS) have gained attention as an eco-friendly solution for water scarcity. However, they face challenges related to low water productivity and scalability. To overcome these challenges, integrating ADS with other desalination technologies can create a small-scale hybrid system. This study proposes integrating ADS with a Thermo Electric Dehumidification (TED) unit to enhance its performance.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
C. X. He, Y. H. Liu, X. Y. Huang, S. B. Wan, Q. Chen, J. Sun, T. S. Zhao
Summary: A decentralized centroid multi-path RC network model is constructed to improve the temperature prediction accuracy compared to traditional RC models. By incorporating multiple heat flow paths and decentralizing thermal capacity, a more accurate prediction is achieved.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chaoying Li, Meng Wang, Nana Li, Di Gu, Chao Yan, Dandan Yuan, Hong Jiang, Baohui Wang, Xirui Wang
Summary: There is an urgent need to shift away from heavy dependence on fossil fuels and embrace renewable energy sources, particularly in the energy-intensive oil refining process. This study presents an innovative concept called the Solar Oil Refinery, which applies solar energy in oil refining. A solar multi-energies-driven hybrid chemical oil refining system that utilizes solar pyrolysis and electrolysis has been developed, significantly improving solar utilization efficiency, cracking rate, and hydrogen yield.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Chao Ma, Guanghui Wang, Dingbiao Wang, Xu Peng, Yushen Yang, Xinxin Liu, Chongrui Yang, Jiaheng Chen
Summary: This study proposes a bio-inspired fish-tail wind rotor to improve the wind power efficiency of the traditional Savonius rotor. Through transient simulations and orthogonal experiments, the key factors affecting the performance are identified. A response surface model is constructed to optimize the power coefficient, resulting in an improvement of 9.4% and 6.6% compared to the Savonius rotor.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sina Bahmanziari, Abbas-Ali Zamani
Summary: This paper proposes a new framework for improving electrical energy harvesting from piezoelectric smart tiles through a combination of magnetic plucking, mechanical impact, and mechanical vibration force mechanisms. Experimental results demonstrate a significant increase in energy yield and average energy harvesting time compared to other mechanisms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Nanjiang Dong, Tao Zhang, Rui Wang
Summary: This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system, and proposes an efficient generating operator to optimize this model. The experimental results show that the proposed algorithm performs better than other state-of-the-art algorithms.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Ahmed E. Mansy, Eman A. El Desouky, Tarek H. Taha, M. A. Abu-Saied, Hamada El-Gendi, Ranya A. Amer, Zhen-Yu Tian
Summary: This study aims to convert office paper waste into bioethanol through a sustainable pathway. The results show that physiochemical and enzymatic hydrolysis of the waste can yield a high glucose concentration. The optimal conditions were determined using the Box-Behnken design, and a blended membrane was used for ethanol purification.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Sven Klute, Marcus Budt, Mathias van Beek, Christian Doetsch
Summary: Heat pumps are crucial for decarbonizing heat supply, and steam generating heat pumps have the potential to decarbonize the industrial sector. This paper presents the current state, technical and economic data, and modeling principles of steam generating heat pumps.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Le Zhang, To-Hung Tsui, Yen Wah Tong, Pruk Aggarangsi, Ronghou Liu
Summary: This study investigates the effectiveness of a current-carrying-coil-based magnetic field in promoting anaerobic digestion of chicken manure. The results show that the applied magnetic field increases methane yield, decreases carbon dioxide production, and reduces the concentration of ammonia nitrogen. Microbial community analysis reveals the enrichment of certain methanogenic genera and enhanced metabolic pathways. Pilot-scale experiments confirm the technical effectiveness of the magnetic field assistance in enhancing anaerobic digestion of chicken manure.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Bo Chen, Ruiqing Ma, Yang Zhou, Rui Ma, Wentao Jiang, Fan Yang
Summary: This paper presents an advanced energy management strategy for fuel cell hybrid electric heavy-duty vehicles, focusing on speed planning and energy allocation. By utilizing predictive co-optimization control, this strategy ensures safe inter-vehicle distance and minimizes energy demand. Simulation results demonstrate the effectiveness of the proposed method in reducing fuel cell degradation cost and overall operation cost.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Fabio Fatigati, Roberto Cipollone
Summary: Organic Rankine Cycle-based microcogeneration systems that use solar sources to generate electricity and hot water can help reduce CO2 emissions in residential energy-intensive sectors. The adoption of a recuperative heat exchanger in these systems improves efficiency, reduces thermal power requirements, and saves on electricity costs.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Lipeng He, Renwen Liu, Xuejin Liu, Xiaotian Zheng, Limin Zhang, Jieqiong Lin
Summary: This research proposes a piezoelectric-electromagnetic hybrid energy harvester (PEHEH) for low-frequency wave motion and self-sensing wave environment monitoring. The PEHEH shows promising power output and the ability to self-power and self-sense the wave environment.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Shangling Chu, Yang Liu, Zipeng Xu, Heng Zhang, Haiping Chen, Dan Gao
Summary: This paper studies a distributed energy system integrated with solar and natural gas, analyzes the impact of different parameters on its energy utilization and emissions reduction, and obtains the optimal solution through an optimization algorithm. The results show that compared to traditional separation production systems, this integrated system achieves higher energy utilization and greater reduction in carbon emissions.
ENERGY CONVERSION AND MANAGEMENT
(2024)
Article
Thermodynamics
Qingpu Li, Yaqi Ding, Guangming Chen, Yongmei Xuan, Neng Gao, Nian Li, Xinyue Hao
Summary: This paper proposes and studies a piston-type thermally-driven pump with a structure similar to a linear compressor, aiming to eliminate the high-quality energy consumption of existing pumps and replace mechanical pumps. The coupling mechanism of working fluid flow and element dimension is analyzed based on force analysis, and experimental data analysis is used to determine the pump operation stroke. Theoretical simulation is conducted to analyze the correlation mechanism of the piston assembly. The research shows that the thermally-driven pump can greatly reduce power consumption and has potential for industrial applications.
ENERGY CONVERSION AND MANAGEMENT
(2024)