Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression

Xu, Xijia; Chen, Ying; Goude, Yannig; Yao, Qiwei

doi:10.1016/j.apenergy.2021.117465

Cited by 17 publications

(2 citation statements)

References 40 publications

(32 reference statements)

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“…It can predict the information for the next time by learning the data of the previous time. Meanwhile, the transferable load has the best flexibility and the better users' satisfaction with electricity consumption than reducible load [22]. It should be noted that the high penetration of wind power and PV in power system cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

Liu

Tian

et al. 2022

International Transactions on Electrical Energy Systems

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To alleviate the issues of global warming and energy crisis, countries are vigorously developing renewable energy technology. The integration of large-scale renewable energy, including wind energy, hydropower, and photovoltaic (PV), has a great impact on system operation scheduling and economic dispatch. This paper presents an economic dispatching method of wind-PV-CSP-hydro-battery system with wind and photovoltaic power generation as the main energy sources. The long short-term memory (LSTM) neural network is applied to predict wind and PV power, besides, the Latin Hypercube Sampling (LHS) method and the synchronous reduction algorithm are used to obtain 10 typical wind and PV power scenarios. A day-ahead economic dispatch model of wind-PV-CSP-hydro-battery mathematical model is established, and relevant constraints are considered. Concentrated solar power (CSP), hydropower stations, batteries, and transferable loads are used as flexible resources to increase the penetration rate of wind and photovoltaic power generation. Finally, three cases are tested to demonstrate the feasibility of the proposed model. The results show that: (1) LSTM neural network can well predict the output power of wind and photovoltaic power generation with a small root mean square error (RMSE). (2) The introduction of transferable loads and CSP power station into the renewable energy power system can effectively reduce the fluctuation and curtailment rates of wind power and PV power generation.

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Section: Introductionmentioning

confidence: 99%

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

Liu

Tian

et al. 2022

International Transactions on Electrical Energy Systems

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“…The real-time inference helps effectively lower energy consumption by reducing energy demand and leveling off the usage peaks. The accurate real-time prediction will also benefit effective scheduling and decision-making in the power system (Xu et al, 2021). Therefore, online analysis of the electricity load time series plays an important role in practice.…”

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confidence: 99%

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data

Xie¹,

Bai²,

Ma³

2023

Preprint

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The Internet of Things (IoT) system generates massive high-speed temporally correlated streaming data and is often connected with online inference tasks under computational or energy constraints. Online analysis of these streaming time series data often faces a trade-off between statistical efficiency and computational cost. One important approach to balance this trade-off is sampling, where only a small portion of the sample is selected for the model fitting and update. Motivated by the demands of dynamic relationship analysis of IoT system, we study the data-dependent sample selection and online inference problem for a multi-dimensional streaming time series, aiming to provide low-cost real-time analysis of high-speed power grid electricity consumption data. Inspired by D-optimality criterion in design of experiments, we propose a class of online data reduction methods that achieve an optimal sampling criterion and improve the computational efficiency of the online analysis. We show that the optimal solution amounts to a strategy that is a mixture of Bernoulli sampling and leverage score sampling. The leverage score sampling involves auxiliary estimations that have a computational advantage over recursive least squares updates. Theoretical properties of the auxiliary estimations involved are also discussed. When applied to European power grid consumption data, the proposed leverage score based sampling methods outperform the benchmark sampling method in online estimation and prediction. The general applicability of the sampling-assisted online estimation method is assessed via simulation studies.

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Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems

You

Cao²,

Wang³

et al. 2022

Applied Energy

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Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression

Cited by 17 publications

References 40 publications

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

Day-Ahead Economic Dispatch of Renewable Energy System considering Wind and Photovoltaic Predicted Output

Optimal Sampling Designs for Multi-dimensional Streaming Time Series with Application to Power Grid Sensor Data

Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems

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