In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.
{"title":"Design of Short-Term Power Load Forecasting Model Based on Deep Neural Network","authors":"Qinwei Duan, Zhu Chao, Cong Fu, Yashan Zhong, Jiaxin Zhuo, Ye Liao","doi":"10.13052/spee1048-5236.43211","DOIUrl":"https://doi.org/10.13052/spee1048-5236.43211","url":null,"abstract":"In power system operation and planning, the accuracy of short-term power load forecasting is very important. Because of its powerful data processing and modeling ability, deep neural network has become an effective tool to accurately predict short-term power load. In this study, a short-term power load prediction model based on deep neural network is designed, which adopts deep long short-term memory and threshold period unit model, and combines Boosting algorithm for model fusion. The results show that the average absolute percentage error of the model fused by Boosting algorithm is 0.07%, which is 1.02% lower than the average weight method and 0.59% lower than the reciprocal error method. Boosting fusion model can effectively reduce the overall prediction error and maintain high stability of prediction error at peak, plateau and time sampling points, so as to achieve good prediction effect. Specifically, the MAPE of the model fused using Boosting algorithm is 0.07% (95% confidence), which is 1.14% higher than the average weight method and 0.79% higher than the reciprocal error method. The design of short-term power load forecasting model based on deep neural network can provide more accurate prediction for power system operation and planning, and help to improve the operation efficiency and reliability of power system. At the same time, the design and application of this model also provide a new idea and method for the application of deep learning in power system. The introduction of Boosting algorithm further improves the prediction accuracy and stability of the model, which is a major innovation in model design.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623222","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4322
Guoliang Bian, Meng Yiqun, Gu Yi, Chuang Liu, Hu Bo, Guiping Zhou, Huanhuan Luo, Yuanzhu Zhao, Yiming Chang, Zhonghui Wang
Monthly centralized bidding is a key link in the transition from annual bilateral trading to spot trading, the research object of this paper is the multi-type power system (coupling system) which is integrated and coupled by thermal power and renewable energy under the same grid point, from the market point of view, this paper discusses its competitive strategy and revenue in the monthly centralized bidding market. First, an outer-level market clearing model that adapts to the participation of the coupling system is constructed to maximize the clearing in terms of social welfare. Secondly, considering the forecast error of scenery, the optimization model of the inner coupling system is established to analyze the cost of the coupling system, and the increment of the coupling system is evaluated quantitatively. Finally, a two-layer optimization model for coupling system to participate in the monthly centralized bidding market is formed, and then the optimal operation strategy of coupling system is studied. The simulation verification of the calculation example shows that participating in the monthly centralized bidding transaction in the mode of the coupling system will increase the income of each of the scenery and fire, the proposed coupling system model promotes changes in the energy structure of the power market, driven by improving the overall economic benefits, ensuring the economic benefits of traditional units and expanding the scope of the renewable energy market, so as to provide electricity to renewable energy and thermal power to improve auxiliary services. The development of the situation provides new ideas for the large-scale grid-connected consumption of new energy.
{"title":"Competitive Pricing Strategy of Wind-Solar-Fire Coupling System in Monthly Concentrated Market Considering the Uncertainty of Renewable Energy","authors":"Guoliang Bian, Meng Yiqun, Gu Yi, Chuang Liu, Hu Bo, Guiping Zhou, Huanhuan Luo, Yuanzhu Zhao, Yiming Chang, Zhonghui Wang","doi":"10.13052/spee1048-5236.4322","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4322","url":null,"abstract":"Monthly centralized bidding is a key link in the transition from annual bilateral trading to spot trading, the research object of this paper is the multi-type power system (coupling system) which is integrated and coupled by thermal power and renewable energy under the same grid point, from the market point of view, this paper discusses its competitive strategy and revenue in the monthly centralized bidding market. First, an outer-level market clearing model that adapts to the participation of the coupling system is constructed to maximize the clearing in terms of social welfare. Secondly, considering the forecast error of scenery, the optimization model of the inner coupling system is established to analyze the cost of the coupling system, and the increment of the coupling system is evaluated quantitatively. Finally, a two-layer optimization model for coupling system to participate in the monthly centralized bidding market is formed, and then the optimal operation strategy of coupling system is studied. The simulation verification of the calculation example shows that participating in the monthly centralized bidding transaction in the mode of the coupling system will increase the income of each of the scenery and fire, the proposed coupling system model promotes changes in the energy structure of the power market, driven by improving the overall economic benefits, ensuring the economic benefits of traditional units and expanding the scope of the renewable energy market, so as to provide electricity to renewable energy and thermal power to improve auxiliary services. The development of the situation provides new ideas for the large-scale grid-connected consumption of new energy.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" 28","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4323
LiaoYi Ning, Kai Liang, Bo Zhang, Yang Gao, Zhilin Xu
This paper presents a solution to the issues of redundancy and ambiguity in predicting variables associated with renewable energy output while aligning with the objectives of the “dual-carbon” energy strategy. A low-carbon economic dispatch method for multi-form energy-intensive parks is proposed, employing the ICT-GRU prediction model. Leveraging historical generation data, the ICT-GRU model enables accurate forecasting of renewable energy output. Subsequently, a comprehensive energy system model is developed considering the carbon emission characteristics and control features of park entities. The model aims to minimize operational costs and facilitate low-carbon economic dispatch. The effectiveness of the proposed method is demonstrated through a case study conducted in a multi-form energy-intensive load park integrated into a power grid. The results validate its capability to achieve low-carbon economic operation and provide valuable insights for grid dispatch optimization.
{"title":"Low-Carbon Economic Dispatch of Integrated Energy Systems in Multi-Form Energy-intensive Parks Based on the ICT-GRU Prediction Model","authors":"LiaoYi Ning, Kai Liang, Bo Zhang, Yang Gao, Zhilin Xu","doi":"10.13052/spee1048-5236.4323","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4323","url":null,"abstract":"This paper presents a solution to the issues of redundancy and ambiguity in predicting variables associated with renewable energy output while aligning with the objectives of the “dual-carbon” energy strategy. A low-carbon economic dispatch method for multi-form energy-intensive parks is proposed, employing the ICT-GRU prediction model. Leveraging historical generation data, the ICT-GRU model enables accurate forecasting of renewable energy output. Subsequently, a comprehensive energy system model is developed considering the carbon emission characteristics and control features of park entities. The model aims to minimize operational costs and facilitate low-carbon economic dispatch. The effectiveness of the proposed method is demonstrated through a case study conducted in a multi-form energy-intensive load park integrated into a power grid. The results validate its capability to achieve low-carbon economic operation and provide valuable insights for grid dispatch optimization.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"99 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4321
Jiayue Wang, Kun Ma, Ling Zhang, Jinghong Wei, Jianzhong Wang
Crude oil, as a strategic energy resource for social development, has witnessed substantial price fluctuations in recent years, impacting the sustainability of economy, ecological environment, and energy security. This paper utilized weekly crude oil price data from January 2003 to March 2023 to examine price bubbles in different markets using the SADF and GSADF methods, identified the duration of price bubbles by comparing the BSADF series with critical value series, assessed the risk of price bubbles in different markets using the established comprehensive indicators, and explored sustainable energy policies from the perspectives of both crude oil importers and exporters. The findings of this study indicate that bubbles exist in the world’s major crude oil markets, with linkages and differences in their origin and termination across different markets. The risk of price bubbles varies widely across different crude oil markets and has significant geographical characteristics, with a higher risk in the markets such as OPEC, Oman, and Dubai, and a lower risk in the markets such as WTI, Brent, and Daqing. This paper proposes sustainable energy policies from the perspective of energy importers and exporters. This is of great importance to enhance the ability of different countries to cope with the risk of crude oil price bubbles and to ensure the sustainability of economic development, ecological environment, and energy security.
{"title":"Crude Oil Price Bubble Identification and Risk Assessment From Different Spot Markets: Establishing a Sustainable Energy Policy","authors":"Jiayue Wang, Kun Ma, Ling Zhang, Jinghong Wei, Jianzhong Wang","doi":"10.13052/spee1048-5236.4321","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4321","url":null,"abstract":"Crude oil, as a strategic energy resource for social development, has witnessed substantial price fluctuations in recent years, impacting the sustainability of economy, ecological environment, and energy security. This paper utilized weekly crude oil price data from January 2003 to March 2023 to examine price bubbles in different markets using the SADF and GSADF methods, identified the duration of price bubbles by comparing the BSADF series with critical value series, assessed the risk of price bubbles in different markets using the established comprehensive indicators, and explored sustainable energy policies from the perspectives of both crude oil importers and exporters. The findings of this study indicate that bubbles exist in the world’s major crude oil markets, with linkages and differences in their origin and termination across different markets. The risk of price bubbles varies widely across different crude oil markets and has significant geographical characteristics, with a higher risk in the markets such as OPEC, Oman, and Dubai, and a lower risk in the markets such as WTI, Brent, and Daqing. This paper proposes sustainable energy policies from the perspective of energy importers and exporters. This is of great importance to enhance the ability of different countries to cope with the risk of crude oil price bubbles and to ensure the sustainability of economic development, ecological environment, and energy security.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4329
Guowei Wang, Chunying Wei, Li Yan, Jian Li
Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.
{"title":"Soil Moisture Prediction Model Based on Improved GRU Recurrent Neural Network","authors":"Guowei Wang, Chunying Wei, Li Yan, Jian Li","doi":"10.13052/spee1048-5236.4329","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4329","url":null,"abstract":"Soil moisture plays a crucial role in land water and energy cycles, and has a certain impact on weather and climate change. In agricultural production, crop moisture status can be determined based on soil moisture, and timely and effective irrigation strategies can be formulated to ensure grain yield while saving water resources, maximizing the value of agricultural water resource utilization, and achieving sustainable development. Therefore, the accuracy of soil moisture prediction has important research value for many fields such as agriculture and climate. In this paper, the super parameters of GRU Recurrent neural network are optimized by intelligent seagull optimization algorithm using a small number of influencing factors, namely, atmospheric temperature, atmospheric humidity, rainfall and soil moisture data, and a soil moisture prediction model is established. The model was used to predict soil moisture for the next 12 hours, 24 hours, 36 hours, and 48 hours, respectively. The final experiment showed that the model in this paper had better predictive effect on soil moisture, with the best predictive evaluation index data being MAPE (12h) = 4.4120%, R2 (12h) = 0.94605, and RMSE (12h) = 1.9998; By comparing the prediction results of multiple time steps vertically, it was found that the prediction accuracy of the model in this paper decreased more smoothly, meeting the requirements of soil moisture prediction.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.43212
Miaomiao Zhu
Sustainable technological innovation can promote this progress in finance. This optimization and upgrading of energy efficiency can provide a good energy environment for green development. The collaborative innovation of financial enterprises has brought such development to the tertiary industry, driving the upgrading of consumer demand, and thus promoting the development of green finance. At this same time, sustainable technological innovation will increase investment in environmental protection, reduce energy consumption, promote the reduction of carbon emissions in the financial industry, and accelerate the development of green finance. This paper uses the data envelopment analysis method to calculate the Marquis production efficiency index, examines the dynamic generation efficiency of intertemporal energy input and output, and calculates the energy efficiency of green finance. This article measures and deconstructs energy efficiency at the provincial level, and pays attention to its changes. The study depicted the efficiency distribution of 28 provinces in two stages of the survey period. By using random kernel estimation for two periods during the survey period, dynamic distribution three-dimensional maps and density contour maps of total factor energy productivity, energy utilization efficiency, and energy allocation efficiency growth rates were drawn for the two periods. The results show that the transfer probability group of energy efficiency and its decomposition terms mainly falls near the diagonal, indicating that TFP and its decomposition term growth rate have certain transferability. From 2005 to 2015, the energy utilization efficiency of the seven economic regions has been significantly improved, and the energy efficiency differences in different regions have gradually converged. The energy efficiency of the Yangtze River Delta and the Pearl River Delta is the highest and has been continuously improved, followed by the Beijing Delta, the central region, the northeast region and the southwest region are again in terms of energy efficiency. Compared with provinces with relatively poor industrial structures, provinces with better industrial structures do not have significant advantages in energy efficiency, while provinces with higher levels of technological innovation typically have relatively higher energy efficiency.
{"title":"The Relationship Between Green Finance, Sustainable Technological Innovation and Energy Efficiency","authors":"Miaomiao Zhu","doi":"10.13052/spee1048-5236.43212","DOIUrl":"https://doi.org/10.13052/spee1048-5236.43212","url":null,"abstract":"Sustainable technological innovation can promote this progress in finance. This optimization and upgrading of energy efficiency can provide a good energy environment for green development. The collaborative innovation of financial enterprises has brought such development to the tertiary industry, driving the upgrading of consumer demand, and thus promoting the development of green finance. At this same time, sustainable technological innovation will increase investment in environmental protection, reduce energy consumption, promote the reduction of carbon emissions in the financial industry, and accelerate the development of green finance. This paper uses the data envelopment analysis method to calculate the Marquis production efficiency index, examines the dynamic generation efficiency of intertemporal energy input and output, and calculates the energy efficiency of green finance. This article measures and deconstructs energy efficiency at the provincial level, and pays attention to its changes. The study depicted the efficiency distribution of 28 provinces in two stages of the survey period. By using random kernel estimation for two periods during the survey period, dynamic distribution three-dimensional maps and density contour maps of total factor energy productivity, energy utilization efficiency, and energy allocation efficiency growth rates were drawn for the two periods. The results show that the transfer probability group of energy efficiency and its decomposition terms mainly falls near the diagonal, indicating that TFP and its decomposition term growth rate have certain transferability. From 2005 to 2015, the energy utilization efficiency of the seven economic regions has been significantly improved, and the energy efficiency differences in different regions have gradually converged. The energy efficiency of the Yangtze River Delta and the Pearl River Delta is the highest and has been continuously improved, followed by the Beijing Delta, the central region, the northeast region and the southwest region are again in terms of energy efficiency. Compared with provinces with relatively poor industrial structures, provinces with better industrial structures do not have significant advantages in energy efficiency, while provinces with higher levels of technological innovation typically have relatively higher energy efficiency.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"84 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4328
Chaoqin Bai, Junrui Liu
Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.
目前，建筑能耗所产生的碳排放占所有碳排放的很大一部分。如何减少碳排放，实现碳中和是当前重要的研究方向。因此，本研究利用 DeST 平台建立了一个气象建筑能耗数据分析预测算法模型，用于节能减排，实现碳中和。新模型利用物联网和云平台技术构建仿真建筑平台，在分析算法中采用支持向量机算法对建筑能耗数据进行向量化处理，可实现建筑能耗和气象数据的归一化处理。通过对建筑能耗数据的处理，可以实现对下一时刻建筑能耗的预测。实验结果表明，新算法的精度和准确度分别比遗传算法高 1 和 0.15，比聚类分析算法分别高 0.6 和 0.07。因此，将该算法模型应用于建筑能耗预测，可以显著提高算法的准确性和精确度。
{"title":"Prediction and Management of Building Energy Consumption Based on Building Environment Simulation Design Platform DeST and Meteorological Data Analysis Algorithm","authors":"Chaoqin Bai, Junrui Liu","doi":"10.13052/spee1048-5236.4328","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4328","url":null,"abstract":"Currently, the carbon emissions of building energy consumption account for a significant portion of all carbon emissions. How to reduce carbon emissions to achieve carbon neutrality is an important current research direction. Therefore this research builds a predictive algorithm model for analyzing energy consumption data of meteorological buildings using DeST platform for energy saving and emission reduction to achieve carbon neutrality. The new model uses Internet of Things and cloud platform technology to build a simulation building platform, and uses the support vector machine algorithm in the analysis algorithm to vectorize building energy consumption data, which can achieve normalization processing of building energy consumption and meteorological data. By processing building energy consumption data, prediction of building energy consumption at the next moment can be achieved. The experimental results show that the precision and accuracy of the new algorithm are higher than genetic algorithm 1 and 0.15 respectively, and 0.6 and 0.07 higher than clustering analysis algorithm respectively. Therefore, applying this algorithm model to building energy consumption prediction can significantly improve the accuracy and precision of the algorithm.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"67 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4325
Maoqiang Zhou
The foundation of the offshore substation mainly adopts the structure of a conductor support frame, and the installation of the upper blocks of the offshore substation mainly employs a lifting vessel for hoisting. The “Zhegen Sha 300 MW Offshore Wind Power Project” adopts a high-pile cap foundation for the offshore substation, making use of a translational installation method for the upper blocks. The innovative foundation design and installation scheme together form the basis of this project, which is explored in detail in this paper. Numerical simulations examining the bearing performance of the high-pile cap foundation during the translational process of the upper blocks are also performed, allowing finer insights into the design and construction of offshore wind power projects.
{"title":"Installation Technique and Numerical Simulation of Stress on High-Pile Footings During the Translation of Offshore Booster Stations","authors":"Maoqiang Zhou","doi":"10.13052/spee1048-5236.4325","DOIUrl":"https://doi.org/10.13052/spee1048-5236.4325","url":null,"abstract":"The foundation of the offshore substation mainly adopts the structure of a conductor support frame, and the installation of the upper blocks of the offshore substation mainly employs a lifting vessel for hoisting. The “Zhegen Sha 300 MW Offshore Wind Power Project” adopts a high-pile cap foundation for the offshore substation, making use of a translational installation method for the upper blocks. The innovative foundation design and installation scheme together form the basis of this project, which is explored in detail in this paper. Numerical simulations examining the bearing performance of the high-pile cap foundation during the translational process of the upper blocks are also performed, allowing finer insights into the design and construction of offshore wind power projects.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":"99 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.43213
Xin Zhao, Changda Huang
This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.
{"title":"Intelligent Processing of Power Operation Data Based on Improved Apriori Algorithm","authors":"Xin Zhao, Changda Huang","doi":"10.13052/spee1048-5236.43213","DOIUrl":"https://doi.org/10.13052/spee1048-5236.43213","url":null,"abstract":"This paper addresses several problems in the power system. Key challenges include low-power information integration, inappropriate system data management, inaccurate system data updating, and inefficient fault diagnosis. We focus on analyzing and diagnosing transmission line faults using the operation data of the power system. The study incorporates the quantitative identification of statements. This is done using the Apriori big data analysis and calculation method. Additionally, we utilize big data analysis and vast power operation data. We aim to achieve automatic analysis and pinpoint the causes of transmission line faults. Furthermore, we seek to optimize the traditional Apriori calculation method. This optimization results in a reduction of about 52% in the candidate item set calculation. The optimized M-Apriori calculation method can analyze the correlation between event index data and faults in real time, and realize automatic diagnosis and analysis of faults through operation data.","PeriodicalId":35712,"journal":{"name":"Strategic Planning for Energy and the Environment","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139623510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-14DOI: 10.13052/spee1048-5236.4324
Xiaolu Wang, Danyue Ni
The refrigeration equipment used to preserve the freshness of farm products in the farm logistics process generates carbon emissions, so the factors affecting the logistics efficiency need to be analyzed to make it sustainable in the context of low-carbon economy. This paper briefly introduced the calculation method of farm product logistics efficiency in the context of low-carbon economy and analyzed the farm product logistics industry in Jiangsu Province from 2011 to 2020. The results suggested that the input-output efficiency of the farm product logistics industry in Jiangsu was relatively high at the level of logistics technology in the context of low-carbon economy, but there was still room for improvement in the expansion scale; the economic level, industrial agglomeration, and industrial structure had a significant positive effect on the efficiency of farm product logistics, and the environmental constraints had a significant inhibitory effect on the efficiency of farm product logistics. Finally, several suggestions were put forward according to the analysis results.
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