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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Frontiers in Energy 2022, Volume 16, Issue 2,   Pages 187-223 doi: 10.1007/s11708-021-0722-7

Abstract: Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity productionNumerous models and techniques have been developed in short, mid and long-term solar forecasting.This paper analyzes some of the potential solar forecasting models based on various methodologies discussed

Keywords： forecasting techniques     hybrid models     neural network     solar forecasting     error metric     support vector machine    

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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Frontiers in Energy 2017, Volume 11, Issue 2,   Pages 175-183 doi: 10.1007/s11708-017-0471-9

Abstract: To take full advantage of the environmental benefits of wind energy, wind power forecasting has to bePower forecasting for regional wind farm groups is the problem that many power system operators care

Keywords： regional wind power forecasting     feature set     minimal-redundancy-maximal-relevance (mRMR)     principal component    

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Conceptual study on incorporating user information into forecasting systems

Jiarui HAN, Qian YE, Zhongwei YAN, Meiyan JIAO, Jiangjiang XIA

Frontiers of Environmental Science & Engineering 2011, Volume 5, Issue 4,   Pages 533-542 doi: 10.1007/s11783-010-0246-6

Abstract: An ideal forecasting system would incorporate user-end information.community has begun to realize that while general improvements to the physical characteristics of weather forecastingThe weather forecasting system should include user interaction because user needs may change with differentA study was conducted on the conceptual forecasting system that included a dynamic, user-oriented interactiveinteractive grand global ensemble) project in China, a case study that was conducted to test the new forecasting

Keywords： user-end information     user-oriented     interactive forecasting system     TIGGE (THORPEX interactive grand global    

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Analysis of US National Research Council’s Persistent Forecasting System of Disruptive Technologies

Zhang Xiaolin

Strategic Study of CAE 2018, Volume 20, Issue 6,   Pages 117-121 doi: 10.15302/J-SSCAE-2018.06.019

Abstract:

The National Research Council’s (NRC) report on Persistent Forecastingof Disruptive Technologies analyzes the key issues of disruptive technology forecasting and proposesan ideal persistent forecasting system model.On this basis, the paper summarizes the connotation and challenges of disruptive technology forecastingFinally, the enlightenment of the research on disruptive technology forecasting is proposed, thus to

Keywords： disruptive technologies     persistent forecasting     ideal forecasting system    

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Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Frontiers in Energy 2016, Volume 10, Issue 1,   Pages 105-113 doi: 10.1007/s11708-016-0393-y

Abstract: This paper proposes the day-ahead electricity price forecasting using the artificial neural networksPrice forecasting is very important for online trading, e-commerce and power system operation.data for the electricity price forecasting tool.The data used for this price forecasting is obtained from the PJM website.forecasting approach.

Keywords： day-ahead electricity markets     price forecasting     load forecasting     artificial neural networks     load serving    

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Forecasting industrial emissions: a monetary approach

Yang DONG, Yi LIU, Jining CHEN, Yebin DONG, Benliang QU

Frontiers of Environmental Science & Engineering 2012, Volume 6, Issue 5,   Pages 734-742 doi: 10.1007/s11783-012-0451-6

Abstract: Forecasts of industrial emissions provide a basis for impact assessment and development planning. To date, most studies have assumed that industrial emissions are simply coupled to production value at a given stage of technical progress. It has been argued that the monetary method tends to overestimate pollution loads because it is highly influenced by market prices and fails to address spatial development schemes. This article develops a land use-based environmental performance index (L-EPI) that treats the industrial land areas as a dependent variable for pollution emissions. The basic assumption of the method is that at a planning level, industrial land use change can represent the change in industrial structure and production yield. This physical metric provides a connection between the state-of-the-art and potential impacts of future development and thus avoids the intrinsic pitfalls of the industrial Gross Domestic Product-based approach. Both methods were applied to examine future industrial emissions at the planning area of Dalian Municipality, North-west China, under a development scheme provided by the urban master plan. The results suggested that the L-EPI method is highly reliable and applicable for the estimation and explanation of the spatial variation associated with industrial emissions.

Keywords： industrial emissions     environmental performance index     spatial planning     industrial land use    

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Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Strategic Study of CAE 2006, Volume 8, Issue 7,   Pages 30-35

Abstract: synthesizes fuzzy optimal selection, BP neural network and genetic algorithm and establishes intelligent forecastingamount of training samples, and gets relative membership degree matrix according to the correlation of forecastingfactors and forecasting objective, then takes the matrix as input of BP neural network to train link-weights, and finally, uses gained link-weight values to verify forecasting.results are highly promising and show that the operation speed, precision and stability of intelligent forecasting

Keywords： fuzzy optimal selection     BP neural network     genetic algorithm     intelligent forecasting mode     mid and longterm intelligent hydrological forecasting    

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Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Strategic Study of CAE 2005, Volume 7, Issue 4,   Pages 83-87

Abstract:

A nonlinear combination forecasting model was established by using neural network and acceleratingExamples were presented finally, as a result, the forecasting precision high in evidence.

Keywords： neural network     accelerating genetic algorithm     nonlinear combination forecasting     forecasting precision    

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A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

Wafaa Mohamed SHABAN; Khalid ELBAZ; Mohamed AMIN; Ayat gamal ASHOUR

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 3,   Pages 329-346 doi: 10.1007/s11709-022-0801-9

Abstract: CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting

Keywords： chloride penetrability     recycled aggregate concrete     machine learning     concrete components     durability    

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Forecasting measured responses of structures using temporal deep learning and dual attention

Frontiers of Structural and Civil Engineering 2024, Volume 18, Issue 6,   Pages 832-850 doi: 10.1007/s11709-024-1092-0

Abstract: The objective of this study is to develop a novel and efficient model for forecasting the nonlinear behavior

Keywords： structural dynamic     time-varying excitation     deep learning     signal processing     response forecasting    

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A novel methodology for forecasting gas supply reliability of natural gas pipeline systems

Feng CHEN, Changchun WU

Frontiers in Energy 2020, Volume 14, Issue 2,   Pages 213-223 doi: 10.1007/s11708-020-0672-5

Abstract: In this paper, a novel systematic and integrated methodology to assess gas supply reliability is proposed based on the Monte Carlo method, statistical analysis, mathematical-probabilistic analysis, and hydraulic simulation. The method proposed has two stages. In the first stage, typical scenarios are determined. In the second stage, hydraulic simulation is conducted to calculate the flow rate in each typical scenario. The result of the gas pipeline system calculated is the average gas supply reliability in each typical scenario. To verify the feasibility, the method proposed is applied for a real natural gas pipelines network system. The comparison of the results calculated and the actual gas supply reliability based on the filed data in the evaluation period suggests the assessment results of the method proposed agree well with the filed data. Besides, the effect of different components on gas supply reliability is investigated, and the most critical component is identified. For example, the 48th unit is the most critical component for the SH terminal station, while the 119th typical scenario results in the most severe consequence which causes the loss of 175.61×10 m gas when the 119th scenario happens. This paper provides a set of scientific and reasonable gas supply reliability indexes which can evaluate the gas supply reliability from two dimensions of quantity and time.

Keywords： natural gas pipeline system     gas supply reliability     evaluation index     Monte Carlo method     hydraulic simulation    

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Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 2,   Pages 177-187 doi: 10.15302/J-FASE-2017177

Abstract: To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model isThe developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop plantingto optimize crops planting area with limited available water resources base on the downstream runoff forecasting

Keywords： crop planting structure optimization     inexact two-stage stochastic programming     runoff forecasting     Shiyang    

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Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 77-80

Abstract: models,  namely SDBP, LMBP and BRBP Model,  are established to carry out the short-term load forecasting

Keywords： short-term load forecasting(STLF)     ANN     Levenberg-Marquardt     Bayesian regularization     optimized algorithms    

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Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Frontiers in Energy 2024, Volume 18, Issue 2,   Pages 223-240 doi: 10.1007/s11708-023-0891-7

Abstract: As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

Keywords： machine learning     lithium-ion battery     state of health     neural network     artificial intelligence    

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Ahead geological forecasting technology of Bieyancao Tunnel on Yichang-Wanzhou Railway

Ren Shaoqiang

Strategic Study of CAE 2010, Volume 12, Issue 8,   Pages 99-106

Abstract: The article introduced that with the help of comprehensive ahead geological forecasting ,this tunnel

Keywords： risky tunnel     Karst cave     underground river     comprehensive ahead geological forecasting    

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Title Author Date Type Operation

A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

Journal Article

Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

Journal Article

Conceptual study on incorporating user information into forecasting systems

Jiarui HAN, Qian YE, Zhongwei YAN, Meiyan JIAO, Jiangjiang XIA

Journal Article

Analysis of US National Research Council’s Persistent Forecasting System of Disruptive Technologies

Zhang Xiaolin

Journal Article

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square

S. Surender REDDY,Chan-Mook JUNG,Ko Jun SEOG

Journal Article

Forecasting industrial emissions: a monetary approach

Yang DONG, Yi LIU, Jining CHEN, Yebin DONG, Benliang QU

Journal Article

Intelligent Forecasting Mode and Approach of Mid and Long Term Intelligent Hydrological Forecasting

Chen Shouyu,Guo Yu,Wang Dagang

Journal Article

Research on Nonlinear Combination Forecasting Approach Based on BP-AGA

Wang Shuo,Zhang Youfu,Jin Juliang

Journal Article

A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

Wafaa Mohamed SHABAN; Khalid ELBAZ; Mohamed AMIN; Ayat gamal ASHOUR

Journal Article

Forecasting measured responses of structures using temporal deep learning and dual attention

Journal Article

A novel methodology for forecasting gas supply reliability of natural gas pipeline systems

Feng CHEN, Changchun WU

Journal Article

Integrated uncertain models for runoff forecasting and crop planting structure optimization of the Shiyang

Fan ZHANG, Mo LI, Shanshan GUO, Chenglong ZHANG, Ping GUO

Journal Article

Short-term Load Forecasting Using Neural Network

Luo Mei

Journal Article

Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion

Journal Article

Ahead geological forecasting technology of Bieyancao Tunnel on Yichang-Wanzhou Railway

Ren Shaoqiang

Journal Article