Cleaner Energy Systems最新文献_第6页

Enhancing exergy efficiency and environmental sustainability in pressure swing azeotropic distillation 提高变压共沸蒸馏的能效和环境可持续性

Cleaner Energy Systems Pub Date : 2024-08-17 DOI: 10.1016/j.cles.2024.100134

Jonathan Wavomba Mtogo , Gladys Wanyaga Mugo , Peter Mizsey

{"title":"Enhancing exergy efficiency and environmental sustainability in pressure swing azeotropic distillation","authors":"Jonathan Wavomba Mtogo , Gladys Wanyaga Mugo , Peter Mizsey","doi":"10.1016/j.cles.2024.100134","DOIUrl":"10.1016/j.cles.2024.100134","url":null,"abstract":"<div><p>This study explores the economic, energetic, exergy efficiency, and environmental benefits of energy integration in pressure-swing distillation, focusing on the separation of tetrahydrofuran/water and acetone/chloroform azeotropes. Heat integration and heat pump techniques are applied to reduce energy consumption. Three energy-efficient configurations are examined, comparing total annual cost (TAC), total energy consumption (TEC), CO<sub>2</sub> emissions, and second-law efficiency. In the tetrahydrofuran/water system, heat integration and heat pump technologies outperform conventional processes, achieving up to 50.2% TAC reduction, 59.6% TEC reduction, 82.8% CO<sub>2</sub> emission reduction, and thermodynamic efficiencies up to 23.5%. In the acetone/chloroform system, similar improvements are observed, with up to 70.9% TAC reduction, 87.2% CO<sub>2</sub> emission reduction, and thermodynamic efficiencies up to 17.6%. These findings demonstrate the effectiveness of energy-saving strategies, endorsing process intensification for environmentally sustainable azeotropic mixture separations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100134"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000281/pdfft?md5=32b3a3a1060b31f4dbda00eec11c1694&pid=1-s2.0-S2772783124000281-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating alternative technologies to diesel generation in India using multi-criteria decision analysis 利用多标准决策分析评估印度柴油发电替代技术

Cleaner Energy Systems Pub Date : 2024-08-17 DOI: 10.1016/j.cles.2024.100133

Ajit Singh , Amruta Joshi , Francis D. Pope , Bhim Singh , Mukesh Khare , Sri Harsha Kota , Jonathan Radcliffe

{"title":"Evaluating alternative technologies to diesel generation in India using multi-criteria decision analysis","authors":"Ajit Singh , Amruta Joshi , Francis D. Pope , Bhim Singh , Mukesh Khare , Sri Harsha Kota , Jonathan Radcliffe","doi":"10.1016/j.cles.2024.100133","DOIUrl":"10.1016/j.cles.2024.100133","url":null,"abstract":"<div><p>Diesel generators (DGs) are widely used in India by business and domestic consumers to provide resilience against unreliable power supplies, but have serious adverse environmental and health impacts. Low carbon alternatives to DGs are becoming more widely available and affordable, though technical and non-technical barriers remain to their widespread adoption. Targeted policy and financial interventions would help accelerate the deployment of these alternatives, where such interventions should be based on local needs. To this end, we use a Multi-Criteria Decision Analysis (MCDA) approach to identify appropriate technology alternatives for DGs in residential, industrial and agricultural applications in India. Within this study, the MCDA framework facilitates evidence-based decision-making through structured discussions with local stakeholders and for evaluating the most suitable option from a variety of available alternatives. Overall, our analysis concluded that a hybrid system combining solar PV and battery storage system are considered most suitable for residential, agricultural as well as industrial applications. This study sets out a pragmatic approach for decision makers considering how to minimise the adverse impacts of DGs while recognising the intricacies of requirements of different applications at a local level. Additionally, our approach showcases how co-creation of potential solutions, and ‘transparency’ in the process, can be accomplished in policy-making, which is critical for wider acceptance of interventions.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100133"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277278312400027X/pdfft?md5=99f45ff1a2e4fb1e7400774ff899a579&pid=1-s2.0-S277278312400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization 利用混合深度学习和基于教学的优化提高风能预测精度

Cleaner Energy Systems Pub Date : 2024-08-17 DOI: 10.1016/j.cles.2024.100139

Mohd Herwan Sulaiman , Zuriani Mustaffa

{"title":"Enhancing wind power forecasting accuracy with hybrid deep learning and teaching-learning-based optimization","authors":"Mohd Herwan Sulaiman , Zuriani Mustaffa","doi":"10.1016/j.cles.2024.100139","DOIUrl":"10.1016/j.cles.2024.100139","url":null,"abstract":"<div><p>Forecasting wind power generation is crucial for ensuring grid security and the competitiveness of the power market. This paper presents an innovative approach that combines deep learning (DL) with Teaching-Learning-Based Optimization (TLBO) to predict wind power output accurately. Using a real dataset spanning diverse weather conditions and turbine specifications collected between January 2018 and March 2020, the study employs 18 features as inputs, including Ambient Temperature, Wind Direction, and Wind Speed, with real power output in kW as the target variable. Metaheuristic algorithms including Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Biogeography-Based Optimization (BBO), and Firefly Algorithm (FA) are comprehensively compared for model optimization. TLBO-DL consistently provides forecasts that closely align with actual wind power values across instances, substantiated by its low RMSE of 98.7601, indicating effective minimization of errors in wind power forecasting. Comparative analysis with other algorithms reveals that TLBO-DL outperforms PSO-DL (RMSE: 102.6627), BMO-DL (RMSE: 132.4839), BBO-DL (RMSE: 103.8517), and FA-DL (RMSE: 104.7282) in terms of overall forecasting accuracy. The variations in the performance of other algorithms across instances highlight the robustness and effectiveness of TLBO-DL in achieving accurate wind power forecasts. Overall, TLBO-DL emerges as a reliable and superior algorithm for wind power forecasting, consistently providing accurate forecasts across a range of instances.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000335/pdfft?md5=a87d785eb724965787507a41bf1ff279&pid=1-s2.0-S2772783124000335-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative study of floating and ground-mounted photovoltaic power generation in Indian contexts 印度浮动和地面光伏发电比较研究

Cleaner Energy Systems Pub Date : 2024-08-17 DOI: 10.1016/j.cles.2024.100140

Anusuya K , Vijayakumar K

{"title":"A comparative study of floating and ground-mounted photovoltaic power generation in Indian contexts","authors":"Anusuya K , Vijayakumar K","doi":"10.1016/j.cles.2024.100140","DOIUrl":"10.1016/j.cles.2024.100140","url":null,"abstract":"<div><p>The escalating global demand for energy and growing environmental concerns have stimulated the development of renewable energy-based power systems. In this context, solar power has gained significant attention, notably in the form of floating photovoltaic systems. These systems, installed on water bodies, not only boost efficiency but also reduce water evaporation from reservoirs. This research explores the power generation capabilities of floating photovoltaic systems in comparison to ground-mounted photovoltaic systems, considering a 250-watt monocrystalline photovoltaic panel. This study utilizes typical meteorological year data to comprehensively analyze four distinct locations in India. By using a single-diode model, this study finds that floating photovoltaic systems provide 6–7 % more power output than ground-mounted photovoltaic systems. This efficiency gain is because the floating photovoltaic panels operate at a lower temperature (4–6 °C) than their ground-mounted photovoltaic counterparts, positively influencing the overall performance. Furthermore, the degradation and soiling of ground-mounted photovoltaic and floating photovoltaic systems were also compared. The financial analysis reveals that ground-mounted photovoltaic systems typically have a lower levelized cost of electricity and shorter payback periods. Even though the financial indicators of floating photovoltaic systems are not favorable compared to ground-mounted photovoltaic systems, these results show how vital floating photovoltaic technology is for achieving the United Nations’ Sustainable Development Goals and how it could be used as an efficient technique to reduce land requirements for solar photovoltaic solutions in various geographical conditions.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100140"},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000347/pdfft?md5=a477c0b72f5feb0b833a97cf97875b34&pid=1-s2.0-S2772783124000347-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-Term forecasting of floating photovoltaic power generation using machine learning models 利用机器学习模型对浮动光伏发电进行短期预测

Cleaner Energy Systems Pub Date : 2024-08-16 DOI: 10.1016/j.cles.2024.100137

Mohd Herwan Sulaiman , Mohd Shawal Jadin , Zuriani Mustaffa , Mohd Nurulakla Mohd Azlan , Hamdan Daniyal

{"title":"Short-Term forecasting of floating photovoltaic power generation using machine learning models","authors":"Mohd Herwan Sulaiman , Mohd Shawal Jadin , Zuriani Mustaffa , Mohd Nurulakla Mohd Azlan , Hamdan Daniyal","doi":"10.1016/j.cles.2024.100137","DOIUrl":"10.1016/j.cles.2024.100137","url":null,"abstract":"<div><p>Floating photovoltaic (FPV) power generation requires accurate short-term forecasting to optimize operational efficiency and enhance grid integration. This study investigates the application of machine learning models for predicting FPV power generation using data from the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) solar installation, which has a capacity of 157.20 kWp. Data were collected at 15-minute intervals from January 15 to January 21, 2024, encompassing nine input features such as ambient temperature, transient horizontal irradiation, daily horizontal irradiation, AC voltages, and AC currents for phases A, B, and C, with the total active power in kW as the target variable. The dataset was divided into a training set (first five days) and a testing set (remaining two days), and five machine learning models—Neural Networks (NN), Random Forest (RF), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)—were employed. The results indicate that the Neural Networks model consistently outperforms the other machine learning algorithms in terms of predictive accuracy. These findings underscore the efficacy of machine learning techniques in forecasting FPV power generation, which has significant implications for enhancing the operational efficiency and grid integration of floating solar installations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000311/pdfft?md5=7ce96141a620bc0a687d5ccbf423c62a&pid=1-s2.0-S2772783124000311-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing energy access in rural areas: Intelligent microgrid management for universal telecommunications and electricity 加强农村地区的能源供应：普及电信和电力的智能微电网管理

Cleaner Energy Systems Pub Date : 2024-08-16 DOI: 10.1016/j.cles.2024.100136

Kanlou Zandjina Dadjiogou , Ayité Sénah Akoda Ajavon , Yao Bokovi

{"title":"Enhancing energy access in rural areas: Intelligent microgrid management for universal telecommunications and electricity","authors":"Kanlou Zandjina Dadjiogou , Ayité Sénah Akoda Ajavon , Yao Bokovi","doi":"10.1016/j.cles.2024.100136","DOIUrl":"10.1016/j.cles.2024.100136","url":null,"abstract":"<div><p>In rural areas lacking an electricity grid, cell phone operators use generators to power their facilities. At the same time, however, the local population is finding it difficult to use the cell phones and other electronic devices for which these operators are deploying their efforts. This situation, due to the problem of access to energy, hinders universal access to telecommunications. The present study aims to solve this problem using microgrid techniques. A microgrid consisting of photovoltaic panels, a genset and storage batteries has been designed to meet the needs of cell phone operators' sites in Bapure, a rural locality in Togo. The focus is on managing energy flows between the various sources of the microgrid, and between the needs of the cell phone operators' site and those of the local population. To resolve the lack of solar irradiation data at Bapure, hourly solar irradiation was predicted using the Adaptive Neuro Fuzzy Inference System (ANFIS) algorithm to obtain a realistic result. Optimization studies were then carried out using the Particle Swarm Optimization (PSO) algorithm to determine the optimum system configuration to ensure continuity of service at the operator's site. The simulation results show that the proposed system has a surplus of energy production at all times, which can be used to supply electricity to the population at a cost equal to 0.0185 USD, with a solar energy utilization rate of 98,95 % and a generator that only needs to operate at 0.15 % throughout the year. The results obtained indicate that a renewable energy system can provide a more efficient solution for electrifying the rural mobile operator's sites and the local population, and can improve the quality of service for the telecommunications industries.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S277278312400030X/pdfft?md5=ae5f5c5d0670b19a102b4e6150630ef8&pid=1-s2.0-S277278312400030X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models 利用机器学习模型分析发电过程中温度对光伏组件表面的影响

Cleaner Energy Systems Pub Date : 2024-08-13 DOI: 10.1016/j.cles.2024.100135

S. M. Rezaul Karim , Debasish Sarker , Md. Monirul Kabir

{"title":"Analyzing the impact of temperature on PV module surface during electricity generation using machine learning models","authors":"S. M. Rezaul Karim , Debasish Sarker , Md. Monirul Kabir","doi":"10.1016/j.cles.2024.100135","DOIUrl":"10.1016/j.cles.2024.100135","url":null,"abstract":"<div><p>Use of fossil fuel in industries causes Carbon emission, which is mostly responsible for global warming. Another aspect is that environment friendly energy production and sustainable development goal is highly dependent on the production of clean energy. According to the IEA solar energy has a huge potential and will contribute up to 16 % of the global electricity by 2050. Hence, prediction of solar energy production has a great deal of demand in renewable energy sector. This paper compares machine-learning algorithms to evaluate the impact of PV module back surface temperature (degC) on the generated power. Support Vector Machine for Regression (SMOreg), Multilayer Perceptron (ANN), Linear Regression, M5 Rules, k-Nearest-Neighbor (Ibk) and Random Forest methods are employed to test their performance in different ratio of training and testing data. The dataset comprises five independent parameters such as PV module back surface temperature (degC), Dry bulb temperature (degC), Relative humidity (%RH), Atmospheric pressure (mb), and Precipitation (mm). The dependent parameter is Maximum power of PV module (W). The correlation coefficient was determined by varying the percentage of training data from 60 % to 85 %. The numerical tests were done for two data sets, one dataset includes all the independent variables and another one excluded the PV module back surface temperature. Except for M5 Rules, other models exhibit consistent correlation coefficients with several of training data. All models demonstrate a dependency on the PV module back surface temperature, with Random Forest surpassing others in overall performance with a correlation coefficient of 0.9713 at 75 % of training set.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100135"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000293/pdfft?md5=e94b18d5f59eadc7771707204dcf1063&pid=1-s2.0-S2772783124000293-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics 智能农业技术：可再生能源、基于物联网的能源管理和精准机器人技术的综合框架

Cleaner Energy Systems Pub Date : 2024-08-11 DOI: 10.1016/j.cles.2024.100132

Anis Ur Rehman , Yasser Alamoudi , Haris M. Khalid , Abdennabi Morchid , S.M. Muyeen , Almoataz Y. Abdelaziz

{"title":"Smart agriculture technology: An integrated framework of renewable energy resources, IoT-based energy management, and precision robotics","authors":"Anis Ur Rehman , Yasser Alamoudi , Haris M. Khalid , Abdennabi Morchid , S.M. Muyeen , Almoataz Y. Abdelaziz","doi":"10.1016/j.cles.2024.100132","DOIUrl":"10.1016/j.cles.2024.100132","url":null,"abstract":"<div><p>Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions to foster sustainability in farming while emphasizing the limitations of conventional farming methods. To address these challenges in modern agriculture systems, this research proposes a comprehensive framework for smart farming. The proposed framework comprises of three technology integrations: 1) an efficient integration of renewable energy resources (RERs) with solar panels and battery energy storage systems (BESS), 2) an IoT-based environmental monitoring for precision irrigation, and 3) an android application-controlled precision robotic system for targeted chemical application. The proposed framework investigates a case study on Sharjah, United Arab Emirates (UAE) to explore and analyze optimal scenarios of multiple energy resources. Results demonstrate successful cross-prototype integration through the Blynk IoT platform providing users with a unified interface. Furthermore, the results provide a comprehensive analysis and investigation into the interactions between RERs and the grid across various combinations. The findings indicate the potential of this framework to revolutionize agriculture and thus offer a sustainable, efficient, and technologically advanced approach. It also represents the contribution of a complete solution to modern agricultural challenges presenting tangible results for a promising future in smart and sustainable farming practices.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100132"},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000268/pdfft?md5=1b376a814067d1973d6aa4c7ad6eeee0&pid=1-s2.0-S2772783124000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data 通过深度学习推进电动汽车电池充电状态估算：使用真实世界驾驶数据的综合研究

Cleaner Energy Systems Pub Date : 2024-08-01 DOI: 10.1016/j.cles.2024.100131

Mohd Herwan Sulaiman , Zuriani Mustaffa , Saifudin Razali , Mohd Razali Daud

{"title":"Advancing battery state of charge estimation in electric vehicles through deep learning: A comprehensive study using real-world driving data","authors":"Mohd Herwan Sulaiman , Zuriani Mustaffa , Saifudin Razali , Mohd Razali Daud","doi":"10.1016/j.cles.2024.100131","DOIUrl":"10.1016/j.cles.2024.100131","url":null,"abstract":"<div><p>Accurately estimating the State of Charge (SOC) in Electric Vehicles (EVs) is critical for battery management and operational efficiency. This paper presents a Deep Learning (DL) approach to address this challenge, utilizing Feed-Forward Neural Networks (FFNN) to estimate SOC in real-world EV scenarios. The research used data from 70 driving sessions with a BMW i3 EV. Each session recorded key factors like voltage, current, and temperature, providing inputs for the DL model. The recorded SOC values served as outputs. We divided the dataset into training, validation, and testing subsets to develop and evaluate the FFNN model. The results demonstrate that the FFNN model yields minimal errors and significantly improves SOC estimation accuracy. Our comparative analysis with other machine learning techniques shows that FFNN outperforms them, with an approximately 2.87 % lower root mean square error (RMSE) compared to the second-best method, Extreme Learning Machine (ELM). This work has significant implications for electric vehicle battery management, demonstrating that deep learning methods can enhance SOC estimation, thereby improving the efficiency and reliability of EV operations.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"8 ","pages":"Article 100131"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000256/pdfft?md5=7448e81d1f5c13869ef75b0f12b0f078&pid=1-s2.0-S2772783124000256-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141949888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm 利用带优化算法的混合深度学习模型进行实时运行的极短期风力发电预测

Cleaner Energy Systems Pub Date : 2024-07-27 DOI: 10.1016/j.cles.2024.100129

Md. Omer Faruque , Md. Alamgir Hossain , Md. Rashidul Islam , S.M. Mahfuz Alam , Ashish Kumar Karmaker

{"title":"Very short-term wind power forecasting for real-time operation using hybrid deep learning model with optimization algorithm","authors":"Md. Omer Faruque , Md. Alamgir Hossain , Md. Rashidul Islam , S.M. Mahfuz Alam , Ashish Kumar Karmaker","doi":"10.1016/j.cles.2024.100129","DOIUrl":"10.1016/j.cles.2024.100129","url":null,"abstract":"<div><p>This paper proposes a new hybrid deep learning model to enhance the accuracy of forecasting very short-term wind power generation. The proposed model comprises a convolutional layer, a long-short-term memory (LSTM) unit, and fully connected neural network. Convolution layer can automatically learn complicated features from the raw input, whereas the LSTM layers can retain useful information through which gradient information may flow over extended periods. To obtain the best performance from the forecasting model, a random search optimization technique has been developed for tuning hyper-parameters of the model developed. The 5 min datasets from the White Rock wind farm, Australia are used to investigate the effectiveness of the proposed model as wind farms are participating in spot electricity market. To compare the effectiveness, the proposed model is compared with the existing models, such as convolution neural network (CNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), artificial neural network (ANN), and support vector machine (SVM). The root-mean-square error (RMSE), mean absolute error (MAE), and Theil’s inequality coefficient (TIC) are used to analyze and compare the performances of the predictive models. Based on RMSE and MAE, the proposed model exhibits a higher accuracy of approximately 23.79% and 28.63% compared to other forecasting methods, respectively.</p></div>","PeriodicalId":100252,"journal":{"name":"Cleaner Energy Systems","volume":"9 ","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772783124000232/pdfft?md5=e04018332afecc00662ddb1533359b4e&pid=1-s2.0-S2772783124000232-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0