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Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization

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ENERGY 2022年第PartA期254卷

作者： Meng, Anbo Wang, Peng Zhai, Guangsong Zeng, Cong Chen, Shun Yang, Xiaoyi Yin, Hao Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Univ Toronto Dept Management Toronto ON M1C 1A4 Canada

Accurate electricity price forecasts is the common concern of market participants. With the integration of high penetration of wind and solar energy resources into the power system, the renewable energy sources will have a great impact on the electricity price volatility undoubtedly. In this regard, a novel attention mechanism (AM) based electricity price forecasting model for electricity market with high proportion of renewable energy is proposed in this paper. In order to investigate the effect of renewable energy on the electricity price prediction, the wind power generation, solar power generation, predicted load and the historical price series are simultaneously taken as the input features. In the data preprocessing stage, the empirical wavelet transform (EWT) is applied to decompose each of the input features into multiple components to avoid learning the autocorrelation of the original sequence. In the model training stage, a hybrid AM-based long short-term memory network (LSTM) is proposed as the forecasting model, aiming to make full use of the AM to dynamically evaluate the importance of different input feature. Furthermore, the crisscross optimization algorithm (CSO) is adopted to retrain the parameters of fully-connected layer so as to further enhance the generalization ability. The proposed method is validated on the datasets of Danish electricity market with a high proportion of renewable energy, and the experimental results show that the proposed model is superior to other hybrid models involved in this study. ?? 2022 Elsevier Ltd. All rights reserved.

关键词： Electricity price forecasting Renewable energy sources crisscross optimization algorithm Empirical wavelet transform Long short-term memory

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Hybrid Whale optimization based Bidirectional Gated Recurrent Unit with Pre-trained CNN model for Software Fault Detection

Hybrid Whale Optimization based Bidirectional Gated Recurren...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Bhardwaj, Arvind Kumar Sameena, H.S. Pareek, Piyush Kumar Bhujang, Raghavi K. Khan, Zabiha Srinivas, Chetana Capgemini HoustonTX United States Global Academy of Technology Dept of Cse Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Centre for Entrepreneurship Ms Ramaiah Institute of Management Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Aiml Bengaluru India Pes University Department of Cse in Ai & Ml RR Campus Bengaluru India

ISBN: (纸本)9798350315455

The deep learning model (DL) S-ResNet-152 (Squeeze-based ResNet-152) is used to pull out the features. Then, a bidirectional gated auto network (Bi-GRU-AN) is used to predict software faults based on a mixed DL model. A modified whale optimization algorithm and a crisscross optimization algorithm (MWOA-CS) are used to find the best weight for the suggested classifier. In MWOA-CS, the position of each dimension of the optimization problem is changed by a random better WOA optimization method while the repetitive process is going on. In this work, experiments are done on two software defect data sets (Kamei and PROMISE) to show how well the model classifies the data. The precision, recall, accuracy, and F1-score are the four complete evaluation measures used. The results show that the framework projected in this paper does a great job of classifying the two types of software fault data sets and is much better than the baseline models. then precision rate as 98.32 and then recall rate as 93.24 and then F-1 score as 94.53 correspondingly. © 2023 IEEE.

关键词： Bidirectional gated auto network component crisscross optimization algorithm formatting insert (key words) Software forecast Software testing resource allocation Squeeze based ResNet-152 style styling

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Prediction of Technical Indicators for Stock Markets using HPO-CCOA Based Machine Learning algorithm

Prediction of Technical Indicators for Stock Markets using H...

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2023 International Conference on Evolutionary algorithms and Soft Computing Techniques, EASCT 2023

作者： Rao, Sakshith S Kumar Pareek, Piyush Vishwanatha, C.R. Kumar Bhardwaj, Arvind Nitte Meenakshi Institute of Technology Department of Master of Computer Applications Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Capgemini Houston Texas United States

ISBN: (纸本)9798350313413

Predicting the Stock Market's behaviour has become an enormous requirement, making it one of the most active study fields. Stock market forecasting is notoriously difficult and calls for a deep dive into the facts. To address this problem and provide a workable solution, we need to employ targeted statistical models and AI-powered algorithms. Several different deep learning and machine learning algorithms can generate a reliable forecast with little room for mistake. Due to the significant degree of uncertainty surrounding technical indicators, they tend to have a disproportionate impact on stock market forecasts. When compared to other methods created for financial market prediction, AI methods demonstrate superior prediction efficiency. In order to forecast the stock market index, this study employs a Machine Learning-based Hyper-parameter tuning optimisation procedure (HPO). During pre-processing, data is first cleaned and normalised to improve classification precision. The crisscross Optimisation algorithm (CCOA) is then used to fine-tune the HPO once K-Nearest Neighbour (KNN) has been applied. Using three publicly accessible datasets, this study presents a thorough analysis of the methods used to forecast stock prices, discusses the difficulties encountered, and looks ahead to the potential for further research in this area. © 2023 IEEE.

关键词： crisscross optimization algorithm Deep learning algorithms Hyper-parameter tuning optimization K-Nearest Neighbour Stock Market Technical indicators

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Horizontal and vertical crossover of Harris hawk optimizer with Nelder-Mead simplex for parameter estimation of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年 223卷 113211-113211页

作者： Liu, Yun Chong, Guoshuang Heidari, Ali Asghar Chen, Huiling Liang, Guoxi Ye, Xiaojia Cai, Zhennao Wangg, Mingjing Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China China Ind Control Syst Cyber Emergency Response T Beijing 100040 Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore Singapore Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Shanghai Lixin Univ Accounting & Finance Shanghai 201209 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

An improved Harris hawks optimization is proposed in this work to facilitate the simulation of an efficient photovoltaic system and extraction of unknown parameters, which combines horizontal and vertical crossover mechanism of the crisscross optimizer and Nelder-Mead simplex algorithm, named CCNMHHO. In CCNMHHO, the cores appeared in the crisscross optimizer are utilized to enrich the information exchange between the individuals and avoid the problem of dimensional stagnation of individuals all through the iterations. Hence, it enhances to change to improve the population quality and prevent the shortcoming of falling into a local optimum. In contrast, the Nelder-Mead simplex algorithm is employed in the proposed CCNMHHO methodology. Nelder-Mead simplex helps to improve individual searching capabilities in performing the local search phase and showing a faster convergence to optimal values. Compared to some algorithms that have a competitive performance in dealing with this type of problem, CCNMHHO has a faster convergence speed, and it shows high stability. In different environments, the experimental data obtained by this improved Harris hawks optimization can reveal a high agreement with the measurement data. The experimental results show that the proposed method not only is very competitive in extracting the unknown parameters of different PV models compared to other state-of-the-art algorithms but also perform well in dealing with the complex outdoor environments such as different temperature and radiance. Therefore, we observed that the CCNMHHO could be considered as a reliable and efficient method in solving a class of cases for the assessment of unknown parameters of solar cells and photovoltaic models. For post-publication guidance, supports, and materials for this research, please refer to the supporting homepage: http://***.

关键词： Photovoltaic models Harris Hawk optimization crisscross optimization algorithm Nelder-Mead simplex algorithm Parameter estimation

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An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization

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ENERGY CONVERSION AND MANAGEMENT 2017年 150卷 108-121页

作者： Yin, Hao Dong, Zhen Chen, Yunlong Ge, Jiafei Lai, Loi Lei Vaccaro, Alfredo Meng, Anbo Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China Univ Sannio Engn Dept I-82100 Benevento Italy

Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.

关键词： Wind power forecasting Secondary hybrid decomposition Empirical mode decomposition Wavelet packet decomposition Extreme learning machine crisscross optimization algorithm

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An Ameliorated Moth-flame optimization algorithm 37

An Ameliorated Moth-flame Optimization Algorithm

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37th Chinese Control Conference (CCC)

作者： Zhao, Xiao-dong Fang, Yi-ming Ma, Zhuang Xu, Miao Yanshan Univ Key Lab Ind Comp Control Engn Hebei Prov Qinhuangdao 066004 Hebei Peoples R China Natl Engn Res Ctr Equipment & Technol Cold Strip Qinhuangdao 066004 Hebei Peoples R China Tangshan Univ Tangshan 063000 Hebei Peoples R China

ISBN: (纸本)9789881563958

To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The Moth-Flame optimization (MFO) algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO, an ameliorated Moth-flame optimization algorithm (A-MFO) that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.

关键词： Moth-flame optimization algorithm crisscross optimization algorithm stochastic optimization convergence

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An efficient crisscross optimization solution to large-scale non-convex economic load dispatch with multiple fuel types and valve-point effects

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ENERGY 2016年第0期113卷 1147-1161页

作者： Meng, Anbo Li, Jinbei Yin, Hao Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

As one of important optimization problems in power system, economic dispatch (ED) with multiple fuel options is characterized by high non-convexity, non-linearity and discontinuity. The, combined action of multiple fuel options and valve-point effects increases the degree of difficulty to solve the ED problem. In this paper, a recently developed heuristic algorithm called crisscross optimization algorithm (CSO) is attempted to address the large-scale and non-convex ED problem with both multiple fuel options and valve-point effects taken into account. The proposed CSO method solves the ED problem through horizontal crossover and vertical crossover. The former searches for the new solutions within a half population of hyper-cubes by adopting a cross-border search approach while the latter provides a unique mechanism to prevent from the premature convergence problems based on the concept of dimensional local minimum. Both operators alternatively generate moderation solutions which are subsequently updated by an elite selection strategy. The proposed method is validated on six test systems consisting of 10-640 generating units and compared with other state-of-the-art methods in the literature. The results show that CSO yields higher quality solutions especially for solving large-scale ED problems with multiple fuel options. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Economic dispatch problem crisscross optimization algorithm Multiple fuel options Valve-point effects Large-scale optimization

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An Ameliorated Moth-flame optimization algorithm

An Ameliorated Moth-flame Optimization Algorithm

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第37届中国控制会议

作者： Xiao-dong Zhao Yi-ming Fang Zhuang Ma Miao Xu Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University National Engineering Research Center for Equipment and Technology of Cold Strip Rolling Tangshan University

To improve the global search ability under the condition of ensuring convergence speed, it is still a major challenge for most meta-heuristic optimization algorithms. The Moth-Flame optimization（MFO） algorithm is an innovative nature-inspired algorithm. To improve the precision of the solution and to quicken the convergence speed and to increase the stability of MFO,an ameliorated Moth-flame optimization algorithm（A-MFO） that combines the crisscross optimization algorithm with MFO is proposed to solve this problems that are mentioned above. The performance of proposed A-MFO is demonstrated on six benchmark mathematical function optimization problems regarding superior accuracy and lower computational time achieved compared to other well-known nature-inspired algorithms.

关键词： Moth-flame optimization algorithm crisscross optimization algorithm stochastic optimization convergence

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