Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories

Gao, Yajing; Zhu, Jing; Hu, Cheng; Xue, Feng; Xie, Qing

doi:10.3390/en9070523

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…The photovoltaic energy production on data center DC i during future time slot t is modeled by a normal law N (Eg i (t), p i (t)) truncated in 0. This model is grounded on numerous scientific works that assume that the forecast error of PV production follows a normal distribution [34], [35], [36], [37], [38]. The values of Eg i and p i for any DC i are then determines according to the trace of local green production of the day.…”

Section: A Expected Brown Consumption and Energy Exchangementioning

confidence: 99%

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Camus

Blavette

Dufossé

et al. 2018

2018 IEEE International Conference on Cluster Computing (CLUSTER)

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The growing appetite of new technologies, such as Internet-of-Things, for Cloud resources leads to an unprecedented energy consumption for these infrastructures. In order to make these energy-hungry distributed systems more sustainable, Cloud providers resort more and more to on-site renewable energy production facilities like photovoltaic panels. Yet, this intermittent and variable electricity production is often uncorrelated with the Cloud consumption induced by its workload. Geographical load balancing, virtual machine (VM) migration and consolidation can be used to exploit multiple Cloud data centers' locations and their associated photovoltaic panels for increasing their renewable energy consumption. However, these techniques cost energy and network bandwidth, and this limits their utilization. In this paper, we propose to rely on the flexibility brought by Smart Grids to exchange renewable energy between distributed sites and thus, to further increase the overall Cloud's self-consumption of the locally-produced renewable energy. Our solution is named SCORPIUS: Self-Consumption Optimization of Renewable energy Production In distribUted cloudS. It takes into account telecommunication network constraints and electrical grid requirements to optimize the Cloud's self-consumption by trading-off between VM migration and renewable energy exchange. Our simulation-based results show that SCORPIUS outperforms existing solutions on various workload traces of production Clouds in terms of both renewable self-consumption and overall energy consumption.

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Section: A Expected Brown Consumption and Energy Exchangementioning

confidence: 99%

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Camus

Blavette

Dufossé

et al. 2018

2018 IEEE International Conference on Cluster Computing (CLUSTER)

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“…Completely rooted from the data samples, non-parametric kernel density estimation method does not need any prior knowledge, which makes it one of the most effective methods to consider the characteristics of DG output. Furthermore, the method has been applied successfully in various areas, including load modeling, wind speed modeling and reliability index calculation [22,23]. In the PSC evaluation based on SA, the non-parametric kernel density estimation method is adopted to model the output of the DG accessed to the system.…”

Section: The Non-parametric Kernel Density Probability Model Of Dgmentioning

confidence: 99%

Evaluating the Effect of Distributed Generation on Power Supply Capacity in Active Distribution System Based on Sensitivity Analysis

et al. 2017

Self Cite

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In active distribution system (ADS), the access of distributed generation (DG) can effectively improve the power supply capacity (PSC). In order to explore the effect of DG on the PSC, the influence of accessed DG on the power supply of ADS has been studied based on generalized sensitivity analysis (SA). On the basis of deriving and obtaining the sensitivity of the evaluation indexes of the PSC to the parameters of connected DG, seeking for the DG access instruction for the purpose of improving the PSC, PSC evaluation model with inserted DG is established based on SA. The change degrees and trends of the PSC and its evaluation indexes caused by the slight increase of DG are calculated rapidly, which provides reference for the planning and operation of ADS. Finally, the feasibility and validity of the proposed theory are validated via IEEE 14-node case study.

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“…For a shortterm PV output forecast, typical climate categories are defined to classify the historical data and the probability density distributions of relative error (RE) are generated for each category. Then the recent RE data are utilized to analyse the error fluctuation conditions of the forecast points so as to obtain the compensation values [25]. In the past, a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improved the prediction of next day closing prices is proposed [26].…”

Section: Introductionmentioning

confidence: 99%

A combination prediction model based on ELM and prior information of shapelet for algal bloom

Liu

Zhang

Wang

et al. 2022

Preprint

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Prediction is crucial to prevent the outbreaks of algal bloom. However, due to the time-varying and nonlinear characteristics of algal bloom, which brings challenges for accurate prediction. Aiming at solving the problem, we consider both prior information and data-driven, and propose an error compensation combination prediction model based on the manifold regularized extreme learning machine fused shapelet (ECM-MRELM-FS). First, the prior information is extracted as the upward shapelet set to discover and predict the value of algal bloom at the typical evolution mode. Meanwhile, a data-driven model of manifold regularized extreme learning machine (MRELM) is used to predict the value of algal bloom at other atypical evolution modes. This switching combination builds a prediction model by MRELM fused the upward shapelet, which can effectively utilize the advantages of prior information and data-driven. Then, an optimized T-S fuzzy inference systems as error compensation model (ECM) based on improved fuzzy c-means clustering is proposed to promote the accuracy of the prediction further. The experiments are performed by two real water quality datasets from USA and China. The final results demonstrate that the proposed combination prediction model can effectively predict algal bloom and provide a feasible and scientific early warning support for the water quality management in lake and reservoir.

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Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories

Cited by 10 publications

References 14 publications

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Self-Consumption Optimization of Renewable Energy Production in Distributed Clouds

Evaluating the Effect of Distributed Generation on Power Supply Capacity in Active Distribution System Based on Sensitivity Analysis

A combination prediction model based on ELM and prior information of shapelet for algal bloom

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