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Short-term power load interval forecasting based on nonparametric Bootstrap errors sampling

ENERGY REPORTS 2022年 8卷 6672-6686页

作者： Xiao, Ling Li, Miaotong Zhang, Shenghui Xuzhou Inst Technol Sch Math & Stat Xuzhou 221018 Peoples R China Chongqing Univ Posts & Telecommun Sch Econ & Management Chongqing 400065 Peoples R China Univ Macau Dept Comp & Informat Sci Org State Key Lab Internet Things Smart City Zhuhai 999078 Macao Peoples R China

Short-term power load forecasting plays a vital role in the planning of distribution network and the development of social economy. Many researchers have devoted their attention to construct point forecasting models. However, the traditional point forecasting regards the forecasting result as a deterministic variable. Since the deviation existed in load forecasting is simply unavoidable and significant, which has great volatility and randomness, point forecast methods are difficult to capture the fluctuation of power load. Probability forecast models are proposed to obtain the uncertain information in the load power. In this paper, firstly, some error information is obtained from the deterministic forecasting results of point forecasting;secondly, the interval of time series data is divided according to the deterministic error information;finally, the Bootstrap method is used to estimate the confidence interval of the deteministic error information to obtain the uncertainty information in the power load data. The instability and randomness of the deterministic error are otained by combining the interval forecasting method so as to improve the accuracy of power load forecasting. Therefore, probability forecasting is combined with point forecasting to obtain more accurate results. The proposed model is used to forecast power load for Queensland, Australia and capital district of New York State. The experimental results show that the proposed method performs better than other comparative models. The experimental results show that the ELM-AdaBoost model has better forecast performance in both long-term and short-term load datasets, and can overcome the seasonality of power load time series data. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Power load interval forecasting Extreme learning machine Nonparametric Bootstrap sampling adaptive boosting algorithm

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A New Method for the Prediction of the Gasoline Yield of the MIP Process

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PETROLEUM SCIENCE AND TECHNOLOGY 2015年第20期33卷 1713-1720页

作者： Jiang, H. Tang, J. Ouyang, F. E China Univ Sci & Technol Res Inst Petr Proc Shanghai 200237 Peoples R China

Themaximizing iso-paraffins (MIP) process, a family craft of fluid catalytic cracking process, is designed to reduce olefin content and increase isoparaffin and aromatic content in gasoline. The authors propose a method used to predict the gasoline yield of MIP process. The prediction model with 17 input variables is based on generalized regression neural network and adaptive boosting (adaboost) algorithm. The mean square error between predictive gasoline yield and actual yield is 2.46.

关键词： catalytic cracking gasoline yield generalized regression neural network adaptive boosting algorithm

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An Ensemble-Learning-Based Method for Short-Term Water Demand Forecasting

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WATER RESOURCES MANAGEMENT 2021年第6期35卷 1757-1773页

作者： Huang, Haidong Zhang, Zhixiong Song, Fengxuan Beibu Gulf Univ Coll Civil Engn & Architecture Qinzhou 535011 Guangxi Peoples R China

Short-term water demand forecasting has always been a hot research topic in the field of water distribution systems, and many researchers have developed a wide variety of methods based on different prediction periodicities. However, few studies have paid attention to using ensemble learning methods for short-term water demand forecasting. In this study, an ensemble-learning-based method was developed to forecast short-term water demand. The proposed method consists of two models: an equal-dimension and new-information model and an ensemble learning model. The purpose of the equal-dimension and new-information model is to update data for modelling periodically, while the ensemble learning model is used for water demand forecasting. To evaluate the forecasting performance, the proposed method was applied to a data set obtained from a real-world water distribution system and compared with the single back-propagation neural network (BPNN) model and the seasonal autoregressive integrated moving average (SARIMA) model. The results show that the proposed method improves both the accuracy and stability of water demand prediction. The proposed method has the potential to provide a promising alternative for short-term water demand forecasting.

关键词： Ensemble learning Water demand forecasting Short-term adaptive boosting algorithm

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Towards an improved Adaboost algorithmic method for computational financial analysis

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2019年 134卷 219-232页

作者： Chang, Victor Li, Taiyu Zeng, Zhiyang Teesside Univ Sch Comp & Digital Technol Middlesbrough Cleveland England Xian Jiaotong Liverpool Univ Int Business Sch Suzhou Suzhou Peoples R China

Machine learning can process data intelligently, perform learning tasks and predict possible outputs in time series. This paper presents the use of our proposed machine learning algorithm;an adaptive boosting (Adaboost) algorithm, in analyzing and forecasting financial nonstationary data, and demonstrating its feasibility in financial trading. The data of future contracts are used in our analysis. The future used to test the Adaboost algorithm is a contract chosen to study future IF1711, which is combined by "HS300 index and Rb", the deformed steel bar future in Chinese stock market. The predicted data is compared with real world data to calculate accuracy and efficiency. The Adaboost algorithm is combined with an Average True Range-Relative Strength Index (ATR-RSI) strategy, so that it can be applied in automatic trading and therefore demonstrate its practical application We develop three additional algorithms to enable optimization, large sale simulations and comparing both the predicted and actual pricing values. We performed experiments and large scale simulations to justify our work. We have tested the accuracy and validity of our approach to improve its quality. In summary, our analysis and results show that our improved Adaboost algorithms may have useful and practical implications in nonstationary data analysis. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Nonstationary data Nonstationary time series Machine learning Back-propagation algorithm adaptive boosting algorithm ATR-RSI strategy

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A Face-Detection Postprocessing Scheme Using a Geometric Analysis for Multimedia Applications

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JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE 2013年第1期13卷 34-42页

作者： Jang, Kyounghoon Cho, Hosang Kim, Chang-Wan Kang, Bongsoon Dong A Univ Dept Elect Engn Pusan South Korea

Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction's low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed.

关键词： Face detection adaptive boosting algorithm face-region stabilization face recognition single-port line memory

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AdaBoost classifiers for phasor measurements-based security assessment of power systems

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IET GENERATION TRANSMISSION & DISTRIBUTION 2018年第8期12卷 1747-1755页

作者： Thirugnanasambandam, Venkatesh Jain, Trapti Indian Inst Technol Indore Discipline Elect Engn Indore 452020 Madhya Pradesh India

Power system security is a major concern in real-time operation. It is essential to protect the system from blackout by taking proper control actions. This study proposes a boosting algorithm for the precise and accurate prediction of static security assessment of power systems using synchronised measurements. In addition to security status, the proposed approach also predicts the type of violations which may be either line overload/voltage violation or both of the insecure operating conditions. To overcome the computational complexity, the number of input phasor measurements is reduced by a statistical approach based on class separability and correlation coefficient indices. In the classification stage, support vector machines (SVMs) are used as weak classifiers and a strong classifier is constructed as the linear combination of many weak SVM classifiers. The performance of the adaptive boosting (AdaBoost) algorithm is further improved by a new weight updation strategy using fuzzy clustering thresholding technique. The efficiency of the proposed approach is demonstrated on IEEE 14-bus, IEEE 30-bus, and Indian 246-bus systems. Further, the test results reveal that the proposed method of security assessment performs better than the other traditional classifiers viz. SVM, feed forward neural network and k-nearest neighbour classifier.

关键词： power system security phasor measurement learning (artificial intelligence) synchronisation computational complexity statistical analysis correlation methods support vector machines pattern clustering fuzzy set theory power engineering computing AdaBoost classifier power system security blackout static security assessment synchronised measurement line overload-voltage violation computational complexity input phasor measurement statistical approach correlation coefficient support vector machine weak SVM classifier adaptive boosting algorithm weight updation strategy fuzzy clustering thresholding technique IEEE 14-bus system IEEE 30-bus system Indian 246-bus system feed forward neural network k-nearest neighbour classifier

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A Practical Method for Floor Water Inrush Prediction Using a Hybrid Artificial Intelligence Model and GIS

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MINE WATER AND THE ENVIRONMENT 2023年第2期42卷 220-229页

作者： Liu, Weitao Han, Mengke Shandong Univ Sci & Technol Coll Safety & Environm Engn Qingdao 266590 Peoples R China Shandong Univ Sci & Technol Coll Energy & Min Engn Qingdao 266590 Peoples R China

Predicting floor water inrush has become increasingly challenging as coal is being mined at greater depths. We established a practical predictive method using a hybrid artificial intelligence (AI) model and geographic information system (GIS) techniques. The hybrid model is a classifier that mixes a back propagation neural network (BPNN) with an adaptive boosting algorithm (AdaBoost). To assess the effectiveness of the model, 33 borehole data points with known water inrush results in the Yangcheng coal mine were used as data samples to train and test the model. The outcomes demonstrated a predictive accuracy of 100%, far exceeding the accuracy and stability of the BPNN classifier alone using the same parameters. Then, GIS techniques were used to extend the approach throughout the mining region;the greatest risk was shown to be in the middle of the area. Given the limited data set, errors may exist in extending the risk predictions for the entire mining area, so more data needs to be collected to ensure the accuracy of subsequent predictions. Still, we believe that the methods and steps adopted in this study can be used to create practical predictive models in different mining regions.

关键词： Back propagation neural network adaptive boosting algorithm Geographic information system Mining

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Iterative Weak Learnability and Multiclass AdaBoost 24

Iterative Weak Learnability and Multiclass AdaBoost

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Cho, In-Koo Libgober, Jonathan A. Ding, Cheng Emory Univ Atlanta GA 30322 USA Hanyang Univ Seoul South Korea Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798400704901

We propose an efficient boosting algorithm for multiclass classification, called ***, that extends SAMME [14] and AdaBoost [4]. The algorithm iteratively applies the weak learnability condition of SAMME to eliminate classes to find the correct classificiation. The iterative weak learnability is a sufficient and necessary condition for boostability, but it is also easier to validate than the EOR criterion of *** [9]. We show that the training error of *** vanishes at the exponential rate, while the generalization error converges to zero at the same rate as AdaBoost. *** numerically outperforms SAMME and achieves performance comparable to *** on benchmark datasets.

关键词： adaptive boosting algorithm Iterative Weak Learnability Boostability Multiclass Classification

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Mechanical properties and global warming potential predictions for high-performance concrete through machine learning models integrated with meta-heuristic algorithms

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MATERIALS TODAY COMMUNICATIONS 2024年 41卷

作者： Song, Yuhua Chongqing Vocat Coll Appl Technol Sch Intelligent Construct & Management Chongqing 401520 Peoples R China

Recent developments in high-performance concrete (HPC) have made it a high-tech material with enhanced durability and properties, and it is a more ecological material with a longer life cycle. Conventional approaches to assessing HPC properties and environmental effects may lack precision in predicting the effects of additional cementitious materials. adaptive boosting algorithm (ADA) and Stochastic Gradient boosting (SGB) have emerged as promising approaches for predicting HPC properties and environmental effects, offering more accurate and efficient alternative methods. In this study, the ADA and SBG models have been employed to predict the two essential HPC mechanical properties, including compressive and tensile strengths (CS and TS) and Global Warming Potential (GWP) by coupling with meta-heuristic algorithms, namely Stadium Spectators Optimizer (SSO), Golden Jackal algorithm (GJA), Brown-bear Optimization algorithm (BOA), and Golf Optimization algorithm (GOA)(sic) for developing hybrid and ensemble prediction approaches. The study assessed the effectiveness of different models for predicting HPC properties and GWP by employing various metrics. The SGSBGG framework showcased remarkable precision and performance compared to other models in the prediction of CS, TS, and GWP. It outshone its counterparts by achieving the highest prediction accuracy, securing R-2 values of 0.994, 0.994, and 0.995 for CS, TS, and GWP predictions, respectively. Additionally, it demonstrated unparalleled accuracy with the lowest modeling errors, registering an RMSE of 1.306 for CS, 0.097 for TS, and 4.469 for GWP. Despite the clear superiority of the SGSBGG model, it is noteworthy that the SGSO, as a hybrid model, delivered notably compatible results.

关键词： High-Performance Concrete Strength Properties Global Warming Potential adaptive boosting algorithm Stochastic Gradient boosting Meta-heuristic algorithms

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Prediction of Coal Seam Permeability by Hybrid Neural Network Prediction Model

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JOURNAL OF ENERGY ENGINEERING 2024年第4期150卷

作者： Wang, Jian Zhao, Mifu Wang, Bowen Wang, Yahua Yang, Gang Ma, Tengfei Xu, Jiafang China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Sinopec Dept Field Dev Northeast Petr Branch Changchun 130062 Peoples R China China United Coalbed Methane Co Ltd 21 Jiuxianqiao Rd Beijing 100011 Peoples R China

Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide guidance for CBM production and prevention of coal mine disasters. In this research, a hybrid neural network prediction model integrating a genetic algorithm, an adaptive boosting algorithm, and a back propagation neural network was developed to predict coal seam permeability. Additional momentum and variable learning rate algorithms were used to improve the learning rate and accuracy of the model, and the model structure was optimized, including the number of hidden layer nodes and the transfer function. The input parameters of the prediction model included gas pressure, compressive strength, reservoir temperature, and effective stress. The corresponding output parameter was coal seam permeability. The correlation between the parameters was calculated. Additionally, a comparative analysis between the proposed prediction model and four other prediction models was carried out to demonstrate the advantages of the proposed model. The results indicated that the correlations between compressive strength, gas pressure, reserve temperature, effective stress, and coal seam permeability were 0.334, -0.148, -0.406, and -0.785, respectively. The proposed prediction model had high accuracy compared with the other prediction models, and its coefficient of determination and root mean squared error were 0.999 and 0.021. Thus, the model can predict coal seam permeability more accurately.

关键词： Coal seam permeability Back propagation neural network Additional momentum algorithm Variable learning rate algorithm Genetic algorithm adaptive boosting algorithm

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