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Point and interval forecasting of real-time and day-ahead electricity prices by a novel hybrid approach

IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第9期11卷 2173-2183页

作者： Tahmasebifar, Reza Sheikh-El-Eslami, Mohammad Kazem Kheirollahi, Reza Tarbiat Modares Univ Dept Elect & Comp Engn Tehran Iran

Accurate forecasting of electricity market prices presents important information to market participants. This provides forward planning of their bidding strategies in order to maximise revenue, profit, and utility perspectives. Nevertheless, due to the non-stationarities involved in market clearing price, an accurate forecasting of these prices is very complex. In this case, transformation from traditional point forecasts to probabilistic interval ones is of great importance to quantify the uncertainties of potential forecasts. In this study, interval forecasting of market clearing prices is conducted based on a novel approach within two consecutive steps. In the first step, a new hybrid method is proposed to estimate point forecasts: combination of wavelet transformation (Wt), feature selection based on Mutual Information (MI), extreme learning machine (ELM), and bootstrap approaches in an ensemble structure is employed. The second step consists of the following stepwise parts: calculating the variance of the model uncertainties based on the extracted data from the ensemble structure, estimating the noise variance by using the maximum-likelihood estimation (MLE), and improving the accuracy of interval forecasting by using particle swarm optimisation (PSO) algorithm. The effectiveness of the proposed approach termed as Wt-mutual information-ELM-MLE-PSO is validated through electricity market real data of Australian electricity network from real-time and day-ahead market viewpoints.

关键词： pricing power markets tendering profitability wavelet transforms feature selection learning (artificial intelligence) statistical analysis maximum likelihood estimation power engineering computing particle swarm optimisation real-time electricity price point and interval forecasting day-ahead electricity price point and interval forcasting electricity market price forecasting bidding strategy forward planning revenue maximisation profit maximisation utility perspective market clearing price interval forecasting wavelet transformation combination feature selection extreme learning machine bootstrap approach model uncertainty variance calculation maximum-likelihood estimation particle swarm optimisation algorithm Wt-mutual information-ELM-MLE-PSO hybrid approach Australian electricity network electricity market real data

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New content-based image retrieval system based on optimised integration of DCD, wavelet and curvelet features

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IET IMAGE PROCESSING 2017年第2期11卷 89-98页

作者： Fadaei, Sadegh Amirfattahi, Rassoul Ahmadzadeh, Mohammad Reza Isfahan Univ Technol Dept Elect & Comp Engn Digital Signal Proc Res Lab Esfahan *** Iran

A new content-based image retrieval (CBIR) scheme is proposed based on the optimised combination of the colour and texture features to enhance the image retrieval precision. This work focuses on a uniform partitioning scheme which is applied in the Hue, Saturation and Value (HSV) colour space to extract dominant colour descriptor (DCD) features. In the proposed CBIR scheme, the DCD features are initially extracted as the colour features, and then an appropriate similarity measure is applied. Also, several wavelet and curvelet features are defined as texture features to overcome the noise and the problem of image translation. Finally, the colour and texture features are optimally combined by using the particle swarm optimisation algorithm. The findings show that not only the proposed colour, wavelet and curvelet features outperform the existing ones but also their optimum combination has a better accuracy in comparison with several contemporary CBIR systems. The performance analysis shows that the proposed method improves the average precision metric from 67.85 to 71.05% for DCD, 58.90 to 65.43% for wavelet and 53.18 to 56.00% for curvelet using Corel dataset. In addition, the optimum combination presents the average precision of %76.50 which is significantly higher than the other state-of-the-art methods.

关键词： image retrieval content-based retrieval wavelet transforms curvelet transforms feature extraction image colour analysis image texture particle swarm optimisation content-based image retrieval system DCD optimised integration curvelet features wavelet features CBIR scheme image texture features image colour features uniform partitioning scheme HSV colour space dominant colour descriptor feature extraction image translation problem particle swarm optimisation algorithm Corel dataset

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Design of the fractional order internal model controller using the swarm intelligence techniques for the coupled tank system

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2021年第2期29卷 1207-1225页

作者： Vavilala, Sateesh Kumar Thirumavalavan, Vinopraba Thota, Radhakrishnan Natarajan, Sivakumaran NIT Puducherry Dept Elect & Elect Engn Karaikal India NIT Tiruchirapalli Dept Chem Engn Trichy India NIT Tiruchirapalli Dept Instrumentat & Control Engn Trichy India

The coupled tank system (comprising two tanks) is used in the chemical industries, water treatment plants etc. Level control of the coupled tank system is a common problem in the process control industry. This work proposes a fractional order internal model controller (FOIMC) with a higher order fractional filter for the level control of the coupled tank system. A first order plus delay time (FOPDT) model of the system is used in the controller design. FOIMC has advantages like robustness to changes in the system gain and extended stability margins. The proposed higher order fractional filter makes the controller physically realizable and quickly roll off the magnitude Bode plot, neglecting the high frequency noise. The particle swarm optimisation (PSO) algorithm is a swarm intelligence based algorithm used for the optimisation problems. The parameters of the FOIMC are optimized with the PSO algorithm by minimizing an objective function constructed using time domain specifications. The novel objective function includes weighted peak overshoot, settling time, and integral square error. A MATLAB (MathWorks, Inc., Natick, MA, USA) based tool, fractional order modelling and control (FOMCON) is used to simulate the fractional order controller. Performance of the proposed FOIMC is compared with two state of the art. Robustness to change in the operating point (tank height) is verified. The proposed FOIMC and the state of the art controllers are implemented on the laboratory setup, and the experimental results are compared.

关键词： Fractional order internal model controller particle swarm optimisation algorithm higher order fractional filter coupled tank system

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Data classification mining of university mental health education resources based on global search algorithm

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2023年第4期73卷 278-287页

作者： Fang, Jing Wang, Daoxun Henan Polytech Marxism Coll Zhengzhou Henan Peoples R China Huanghe Sci & Technol Univ Vocat & Tech Coll Zhengzhou Henan Peoples R China

To solve the problems of low accuracy and recall rate, as well as long classification mining time in traditional methods, a university mental health education resource data classification mining method based on global search algorithm is proposed. Collect data on university mental health education resources, identify abnormal data using isolated forests and perform correction processing. Extract resource data features using Fisher discriminant criteria and select data features. Build a data classification mining model for university mental health education resources, and use the particle swarm optimisation algorithm in the global search algorithm to construct an optimisation objective function for classification mining. Input the data to be processed into the optimised model to obtain relevant classification mining results. The experimental results show that the proposed method has a mean classification mining accuracy of 98.1%, a mean recall rate of 97.3% and a classification mining time of less than 1.28 s.

关键词： global search algorithm university mental health education resource classification mining isolation forests fisher's discriminant criterion particle swarm optimisation algorithm

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Effective task scheduling for heterogeneous distributed systems using firefly algorithm

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2015年第2期11卷 132-142页

作者： Eswari, R. Nickolas, S. Natl Inst Technol Dept Comp Applicat Tiruchirappalli 620015 Tamil Nadu India

Achieving minimum execution time for any application with better resource utilisation is a major challenge in heterogeneous distributed systems. But the performance can be exploited in these systems through proper scheduling of application tasks. An efficient meta-heuristic algorithm called firefly algorithm is applied in this paper to solve static task scheduling problem in heterogeneous systems. The social behaviour of fireflies is mimicked to generate optimal task schedule length. The efficiency of the firefly-based task scheduling algorithm is compared with the existing particle swarm optimisation-based scheduling algorithm. The experimental results show that the firefly algorithm-based approach gives better results when compared to PSO algorithm and performs well with minimum processors for effective scheduling of tasks.

关键词： task scheduling heterogeneous systems firefly algorithm particle swarm optimisation algorithm

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Analysis and control for ideal variable transmission ratio characteristics of active front wheel steering

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2024年第1期45卷 40-57页

作者： He, Xiaojun Yang, Kun Chang, Yile Ma, Chao Wang, Wei Wang, Jie Shandong Univ Technol Sch Transportat & Vehicle Engn Zibo 255000 Peoples R China SAIC Motor R&D Innovat Headquarters Shanghai 201800 Peoples R China CATARC Automot Test Ctr Tianjin Co Ltd Tianjin 300300 Peoples R China

The change law of ideal transmission ratio with vehicle speed and hand-wheel angle is studied based on constant gain of steady-state vehicle system. It is used to resolve the conflict between steering sensitivity at the low-speed segment and steering stability at the high-speed segment for the traditional vehicle. For preventing the sudden change of hand-wheel torque caused by the transmission ratio change, the ideal variable transmission ratio (VTR) law is fitted by the improved S-type function and optimised by particle swarm optimisation (PSO) algorithm. For improving driving stability of vehicle, the stability control strategy based on linear quadratic regulator (LQR) is studied based on the optimised ideal variable transmission ratio control law. The front wheel angle is decided by the vehicle stability control strategy, and then the active front wheel steering (AFS) motor angle is obtained by the AFS calculation module to realise the AFS control. The closed-loop driver-vehicle system is established. This system includes driver model, the vehicle dynamic model, AFS model and so on. The results indicate that the performance of the proposed controller is good in the front wheel steering angle control for the better tracking to desired vehicle state.

关键词： ideal variable transmission ratio improved S-type function particle swarm optimisation algorithm linear quadratic regulator LQR active front wheel steering AFS

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Unsupervised clustering algorithm for databases based on density peak optimisation

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INTERNATIONAL JOURNAL OF AUTONOMOUS AND ADAPTIVE COMMUNICATIONS SYSTEMS 2023年第3期16卷 313-326页

作者： Pu, Xiaochuan Seo, Wonchul Ruan, Qingqiang Zunyi Normal Univ Coll Informat Engn Guizhou 563002 Zunyi Peoples R China Pukyong Natl Univ Grad Sch Management Technol Busan 48513 South Korea

In order to improve the precision and sensitivity of traditional unsupervised clustering algorithms, an unsupervised clustering algorithm based on density peak optimisation is proposed. K-nearest neighbour is used to set a new method to measure the sample density and sample distance. The selected sample is the initial cluster centre, and the number of clusters is automatically determined. The improved K-means algorithm and particle swarm optimisation algorithm are introduced to optimise the convergence process of the algorithm. Experimental results show that compared with the traditional algorithm, the clustering accuracy of the proposed algorithm can be stable at 95-100%, and the sensitivity of the algorithm is also relatively ideal. With the increase in the number of data genes, the sensitivity is always above 95%. The running time is about 0.2 min, and the data show that the proposed algorithm meets the requirements of the current application field.

关键词： density peak optimisation database unsupervised clustering initial cluster centre K-means algorithm particle swarm optimisation algorithm

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Short-term forecasting method for lighting energy consumption of large buildings based on time series analysis

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INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES 2023年第3期45卷 220-232页

作者： Li, Yanpeng Zhang, Guofeng Univ Sci & Technol Liaoning Dept Environm Design Anshan Liaoning Peoples R China Univ Sci & Technol Liaoning Dept Architecture Anshan Liaoning Peoples R China

In order to overcome the problems of high data noise, low prediction accuracy and long prediction time in the traditional short-term prediction method of lighting energy consumption of large buildings, a short-term prediction method of lighting energy consumption of large buildings based on time series analysis is proposed in this paper. The improved threshold function is used to denoise the data, and the fuzzy c-means clustering algorithm is used to cluster the denoised data. The time series analysis method is used to construct the self-excitation threshold autoregressive model. When the model parameters are optimal, the clustered data are input into the model to output the short-term prediction results of lighting energy consumption of large buildings. The experimental results show that compared with the traditional method, the average data noise of this method is 12.3 dB, the prediction accuracy remains above 94% and the average prediction time is only 57 ms.

关键词： time series analysis large buildings lighting energy consumption short-term forecast fuzzy c-means clustering algorithm self-excitation threshold autoregressive model particle swarm optimisation algorithm

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Simulation for Path Planning of SLOCUM Glider in Near-bottom Ocean Currents Using Heuristic algorithms and Q-Learning

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DEFENCE SCIENCE JOURNAL 2015年第3期65卷 220-225页

作者： Gautam, Utkarsh Ramanathan, Malmathanraj JSS Acad Tech Educ Dept Instrumentat & Control Engn Noida Uttar Pradesh India Natl Inst Technol Dept ECE Tiruchirappalli Tamil Nadu India

Addressing the need for exploration of benthic zones utilising autonomous underwater vehicles, this paper presents a simulation for an optimised path planning from the source node to the destination node of the autonomous underwater vehicle SLOCUM Glider in near-bottom ocean environment. Near-bottom ocean current data from the Bedford Institute of Oceanography, Canada, have been used for this simulation. A cost function is formulated to describe the dynamics of the autonomous underwater vehicle in near-bottom ocean currents. This cost function is then optimised using various biologically-inspired algorithms such as genetic algorithm, Ant Colony optimisation algorithm and particle swarm optimisation algorithm. The simulation of path planning is also performed using Q-learning technique and the results are compared with the biologically-inspired algorithms. The results clearly show that the Q-learning algorithm is better in computational complexity than the biologically-inspired algorithms. The ease of simulating the environment is also more in the case of Q-learning techniques. Hence this paper presents an effective path planning technique, which has been tested for the SLOCUM glider and it may be extended for use in any standard autonomous underwater vehicle.

关键词： Simulation path planning AUV SLOCUM Glider near-bottom ocean currents Q-learning genetic algorithm ant colony optimisation algorithm particle swarm optimisation algorithm

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Frequency regulation of multi-area power systems with plug-in electric vehicles considering communication delays

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第14期10卷 3481-3491页

作者： Fan, Hua Jiang, Lin Zhang, Chuan-Ke Mao, Chengxiong Huazhong Univ Sci & Technol State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ Merseyside England China Univ Geosci Sch Automat Wuhan 430074 Peoples R China

Dynamics of vehicle-to-grid service to frequency regulation may suffer from the uncertainties arising from the spatial and temporal diversities of the large-scale plug-in vehicles (PEVs). Communication delays incurred both in the aggregation of the PEVs and in load frequency control (LFC) loop also degrade the dynamics and even destabilise the system. This study designs the LFC scheme considering those uncertainties. First, a dynamic model of individual PEV based on equivalent circuit and an aggregated model for PEVs group considering driving behaviours, battery characteristics, and delays are developed. Then, a state-space model with time delays and uncertainties is constructed to model the closed-loop LFC scheme with PEVs in primary control loop. After constructing the relationships among control gains, robust performances, and delays, a robust proportional-integral-derivative-type LFC scheme design method is developed by using linear matrix inequality and particle swarm optimisation algorithm. Case studies based on a three-area LFC system demonstrate that the PEVs contribute to the frequency regulation under different load disturbances and that the proposed LFC scheme successfully suppresses frequency fluctuations in the presence of delays and provides robustness against the uncertainties arising from the PEVs.

关键词： frequency control electric vehicles load regulation closed loop systems equivalent circuits state-space methods delays three-term control linear matrix inequalities particle swarm optimisation frequency regulation multiarea power system plug-in electric vehicle communication delay vehicle-to-grid service PEV load frequency control closed-loop system Thevenin equivalent circuit battery characteristic state-space model time delay robust proportional-integral-derivative-type LFC scheme design linear matrix inequality technique particle swarm optimisation algorithm long-term random load demand frequency fluctuation suppression

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