A parallel evolutionary extreme learning machine scheme for electrical load prediction

Chen, Peikun; Li, Wanhua; Chen, Yuzhong; Guo, Kun; Niu, Yuzhen

doi:10.1109/sai.2017.8252123

Cited by 4 publications

(4 citation statements)

References 27 publications

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“…In this paper, the speed-up factor S speedup is defined as an evaluation index for measuring the parallelization degree of the big data platform, as shown in (25):…”

Section: Processing Time Analysis Of Different Real-time Evscf Data Smentioning

confidence: 99%

“…Group two is the combination of clustering or optimization algorithms and traditional ML algorithms [22][23][24]. Group three is a combination of group one and group two [25][26][27]. In group one, the parallelization of the algorithm effectively improves the computing speed and accuracy of load forecasting in parallel computing framework MapReduce and Spark.…”

Section: Introductionmentioning

confidence: 99%

“…In group two, clustering algorithms on large-scale data sets can be used to improve the performance markedly [22,23]. For group three, [25] and [26] propose new hybrid algorithms, which combine the improved particle swarm optimization and extreme learning machine, fuzzy C clustering and support vector machines (SVM), respectively. The problems of over-fitting and long training time caused by the increase of data scale are faced by multi-distributed back propagation (BP) neural networks [27].…”

Section: Introductionmentioning

confidence: 99%

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Schedulable capacity forecasting for electric vehicles based on big data analysis

Mao

Zhang

Chang

et al. 2019

J. Mod. Power Syst. Clean Energy

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Fast and accurate forecasting of schedulable capacity of electric vehicles (EVs) plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems. Traditional methods are insufficient to deal with large-scale actual schedulable capacity data. This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales. The time scale of these data analysis comprises the real time of one minute, ultra-short-term of one hour and one-day-ahead scale of 24 hours. The predicted results for different time scales can be used for various ancillary services. The proposed algorithm is validated using operation data of 521 EVs in the field. The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm, the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.

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“…In this paper, the speed-up factor S speedup is defined as an evaluation index for measuring the parallelization degree of the big data platform, as shown in (25):…”

Section: Processing Time Analysis Of Different Real-time Evscf Data Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Schedulable capacity forecasting for electric vehicles based on big data analysis

Mao

Zhang

Chang

et al. 2019

J. Mod. Power Syst. Clean Energy

View full text Add to dashboard Cite

show abstract

“…a season or a year. Estimate of the average power P ave n can be obtained by widely used load forecasting methods; see [20]. Apart from this operational condition, due to the practical limits of outage duration d n and number of outages k n , we also have the following two box constraints…”

Section: B Operational Constraintsmentioning

confidence: 99%

Optimal Rotational Load Shedding Via Bilinear Integer Programming

Maqsood

Corzine

2018

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)

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This paper addresses the problem of managing rotational load shedding schedules for a power distribution network with multiple load zones. An integer optimization problem is formulated to find the optimal number and duration of planned power outages. Various types of damage costs are proposed to capture the heterogeneous load shedding preferences of different zones. The McCormick relaxation along with an effective procedure feasibility recovery is developed to solve the resulting bilinear integer program, which yields a high-quality suboptimal solution. Extensive simulation results corroborate the merit of the proposed approach, which has a substantial edge over existing load shedding schemes.

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Evolutionary ELMs with Alternative Treatments for the Population Out-Bounded Individuals

Pacífico

Ludermir

Oliveira

2018

2018 7th Brazilian Conference on Intelligent Systems (BRACIS)

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A parallel evolutionary extreme learning machine scheme for electrical load prediction

Cited by 4 publications

References 27 publications

Schedulable capacity forecasting for electric vehicles based on big data analysis

Schedulable capacity forecasting for electric vehicles based on big data analysis

Optimal Rotational Load Shedding Via Bilinear Integer Programming

Evolutionary ELMs with Alternative Treatments for the Population Out-Bounded Individuals

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