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Regional wind power forecasting model with NWP grid data optimized

Zhao WANG, Weisheng WANG, Bo WANG

《能源前沿（英文）》 2017年第11卷第2期页码 175-183 doi: 10.1007/s11708-017-0471-9

摘要： Unlike the traditional fossil energy, wind, as the clean renewable energy, can reduce the emission of the greenhouse gas. To take full advantage of the environmental benefits of wind energy, wind power forecasting has to be studied to overcome the troubles brought by the variable nature of wind. Power forecasting for regional wind farm groups is the problem that many power system operators care about. The high-dimensional feature sets with redundant information are frequently encountered when dealing with this problem. In this paper, two kinds of feature set construction methods are proposed which can achieve the proper feature set either by selecting the subsets or by transforming the original variables with specific combinations. The former method selects the subset according to the criterion of minimal-redundancy-maximal-relevance (mRMR), while the latter does so based on the method of principal component analysis (PCA). A locally weighted learning method is also proposed to utilize the processed feature set to produce the power forecast results. The proposed model is simple and easy to use with parameters optimized automatically. Finally, a case study of 28 wind farms in East China is provided to verify the effectiveness of the proposed method.

关键词： regional wind power forecasting feature set minimal-redundancy-maximal-relevance (mRMR) principal component analysis (PCA) locally weighted learning model

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A comprehensive review and analysis of solar forecasting techniques

Pardeep SINGLA, Manoj DUHAN, Sumit SAROHA

《能源前沿（英文）》 2022年第16卷第2期页码 187-223 doi: 10.1007/s11708-021-0722-7

摘要： In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous 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 in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problem-solving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

关键词： forecasting techniques hybrid models neural network solar forecasting error metric support vector machine (SVM)

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Conceptual study on incorporating user information into forecasting systems

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

《环境科学与工程前沿（英文）》 2011年第5卷第4期页码 533-542 doi: 10.1007/s11783-010-0246-6

摘要： The purpose of improving weather forecast is to enhance the accuracy in weather prediction. An ideal forecasting system would incorporate user-end information. In recent years, the meteorological community has begun to realize that while general improvements to the physical characteristics of weather forecasting systems are becoming asymptotically limited, the improvement from the user end still has potential. The weather forecasting system should include user interaction because user needs may change with different weather. A study was conducted on the conceptual forecasting system that included a dynamic, user-oriented interactive component. This research took advantage of the recently implemented TIGGE (THORPEX interactive grand global ensemble) project in China, a case study that was conducted to test the new forecasting system with reservoir managers in Linyi City, Shandong Province, a region rich in rivers and reservoirs in eastern China. A self-improving forecast system was developed involving user feedback throughout a flood season, changing thresholds for flood-inducing rainfall that were responsive to previous weather and hydrological conditions, and dynamic user-oriented assessments of the skill and uncertainty inherent in weather prediction. This paper discusses ideas for developing interactive, user-oriented forecast systems.

关键词： user-end information user-oriented interactive forecasting system TIGGE (THORPEX interactive grand global ensemble)

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美国NRC颠覆性技术持续预测系统浅析

张晓林

《中国工程科学》 2018年第20卷第6期页码 117-121 doi: 10.15302/J-SSCAE-2018.06.019

摘要：

美国国家研究委员会（NRC）发布的《颠覆性技术持续性预测》（Persistent Forecasting of Disruptive Technologies

关键词：颠覆性技术持续预测理想系统

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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

《能源前沿（英文）》 2016年第10卷第1期页码 105-113 doi: 10.1007/s11708-016-0393-y

摘要： This paper proposes the day-ahead electricity price forecasting using the artificial neural networks (ANN) and weighted least square (WLS) technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices (LMP) in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach proposed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland (PJM) interconnection is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

关键词： day-ahead electricity markets price forecasting load forecasting artificial neural networks load serving entities

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Forecasting industrial emissions: a monetary approach

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

《环境科学与工程前沿（英文）》 2012年第6卷第5期页码 734-742 doi: 10.1007/s11783-012-0451-6

摘要： 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.

关键词： industrial emissions environmental performance index spatial planning industrial land use

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智能预报模式与水文中长期智能预报方法

陈守煜,郭瑜,王大刚

《中国工程科学》 2006年第8卷第7期页码 30-35

摘要：

建立了以模糊优选、BP神经网络及遗传算法有机结合的智能预报模式与方法。在应用该方法进行中长期水文智能预报时，首先选取训练样本的数量，根据预报因子与预报对象的相关关系得到相对隶属度矩阵;再将其作为BP神经网络输入值以训练连接权重；最后将得到的连接权重值用于预报检验。计算结果表明，智能预报模式与方法的运行速度、精度及稳定性都达到了实际应用的要求。

关键词：模糊优选 BP神经网络遗传算法智能预报模式中长期水文智能预报

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基于BP-AGA的非线性组合预测方法研究

王硕,张有富,金菊良

《中国工程科学》 2005年第7卷第4期页码 83-87

摘要：

运用神经网络和加速遗传算法建立非线性组合预测模型，在BP算法训练网络出现收敛速度缓慢时启用加速遗传算法（AGA）来优化网络参数，把AGA的优化结果作为BP算法的初始值，再用BP算法训练网络，如此交替运行BP算法和AGA以加快网络的收敛速度，同时改善局部最小问题。最后给出实例研究，结果表明，该方法能明显提高预测精度。

关键词：神经网络加速遗传算法非线性组合预测预测精度

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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

《结构与土木工程前沿（英文）》 2022年第16卷第3期页码 329-346 doi: 10.1007/s11709-022-0801-9

摘要： This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.

关键词： chloride penetrability recycled aggregate concrete machine learning concrete components durability

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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

《农业科学与工程前沿（英文）》 2018年第5卷第2期页码 177-187 doi: 10.15302/J-FASE-2017177

摘要： To improve the accuracy of runoff forecasting, an uncertain multiple linear regression (UMLR) model is presented in this study. The proposed model avoids the transfer of random error generated in the independent variable to the dependent variable, as this affects prediction accuracy. On this basis, an inexact two-stage stochastic programming (ITSP) model is used for crop planting structure optimization (CPSO) with the inputs that are interval flow values under different probabilities obtained from the UMLR model. The developed system, in which the UMLR model for runoff forecasting and the ITSP model for crop planting structure optimization are integrated, is applied to a real case study. The aim of the developed system is to optimize crops planting area with limited available water resources base on the downstream runoff forecasting in order to obtain the maximum system benefit in the future. The solution obtained can demonstrate the feasibility and suitability of the developed system, and help decision makers to identify reasonable crop planting structure under multiple uncertainties.

关键词： crop planting structure optimization inexact two-stage stochastic programming runoff forecasting Shiyang River Basin uncertain multiple linear regression

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

Feng CHEN, Changchun WU

《能源前沿（英文）》 2020年第14卷第2期页码 213-223 doi: 10.1007/s11708-020-0672-5

摘要： 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.

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

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

《能源前沿（英文）》 doi: 10.1007/s11708-023-0891-7

摘要： 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.

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

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应用神经网络进行短期负荷预测

罗枚

《中国工程科学》 2007年第9卷第5期页码 77-80

摘要：

以某地区购网有功功率的负荷数据为背景，建立了３个BP神经网络负荷预测模型———SDBP，LMBP 及BRBP模型进行短期负荷预测工作，并对其结果进行比较。针对传统的BP算法具有训练速度慢，易陷入局部最小点的缺点，采用具有较快收敛速度及稳定性的L-M（Levenberg-Marquardt）优化算法进行预测，使平均相对误差有了很大改善，而采用贝叶斯正则化算法可以解决网络过度拟合，提高网络的推广能力。

关键词：短期负荷预测人工神经网络 L唱M算法贝叶斯正则化算法优化算法

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宜万铁路别岩槽隧道地质超前预报综合技术

任少强

《中国工程科学》 2010年第12卷第8期页码 99-106

摘要：

别岩槽隧道为宜万铁路首批开工的重难点、咽喉工程，被列为宜万铁路I级风险隧道。该隧道地质极为复杂，尤其是溶洞和地下暗河非常发育，被形象地称施工中可能有“六冒”现象发生。文章介绍了该隧道利用综合地质超前预报技术，避免了地质灾害事故的发生，保证了安全施工，并结合实践，总结了高风险隧道的地质超前预报综合技术。

关键词：风险隧道溶洞暗河综合超前预报

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Integrated virtual-design methods for forecasting radiated noise of single cylinder diesel block

GUO Lei, HAO Zhiyong, XU Hongmei, LIU Lianyun

《能源前沿（英文）》 2008年第2卷第4期页码 416-421 doi: 10.1007/s11708-008-0097-z

摘要： The two cycle dynamical results, such as the bearing load, the piston thrust, and the load spatial distribution etc., were obtained by hybrid dynamical simulation of the flexible assembly of the block and crank-train. The finite element model of the block was validated by modal test. The frequency response of the block was calculated using the finite element method (FEM). Finally, the radiated noise such as sound power level and efficiency in the out sound field were obtained using the direct boundary element method (BEM).

关键词： bearing dynamical frequency response dynamical simulation hybrid dynamical