Statistical Load Forecasting Using Optimal Quantile Regression Random Forest and Risk Assessment Index

Aprillia, Happy; Yang, Hong-Tzer; Huang, Chao-Ming

doi:10.1109/tsg.2020.3034194

Cited by 81 publications

(29 citation statements)

References 33 publications

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“…Many researchers have made unremitting efforts to improve the accuracy of power load forecasting in the environment of smart distribution networks. The different forecasting principles can be divided into traditional forecasting methods based on statistics [7,8] and intelligent forecasting methods based on machine learning algorithms [9,10]. Traditional methods of forecasting electrical loads have the advantage of low computational effort and high prediction accuracy for simple linear cases.…”

Section: Literature Reviewmentioning

confidence: 99%

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

et al. 2021

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Inaccurate electricity load forecasting can lead to the power sector gaining asymmetric information in the supply and demand relationship. This asymmetric information can lead to incorrect production or generation plans for the power sector. In order to improve the accuracy of load forecasting, a combined power load forecasting model based on machine learning algorithms, swarm intelligence optimization algorithms, and data pre-processing is proposed. Firstly, the original signal is pre-processed by the VMD–singular spectrum analysis data pre-processing method. Secondly, the noise-reduced signals are predicted using the Elman prediction model optimized by the sparrow search algorithm, the ELM prediction model optimized by the chaotic adaptive whale algorithm (CAWOA-ELM), and the LSSVM prediction model optimized by the chaotic sparrow search algorithm based on elite opposition-based learning (EOBL-CSSA-LSSVM) for electricity load data, respectively. Finally, the weighting coefficients of the three prediction models are calculated using the simulated annealing algorithm and weighted to obtain the prediction results. Comparative simulation experiments show that the VMD–singular spectrum analysis method and two improved intelligent optimization algorithms proposed in this paper can effectively improve the prediction accuracy. Additionally, the combined forecasting model proposed in this paper has extremely high forecasting accuracy, which can help the power sector to develop a reasonable production plan and power generation plans.

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Section: Literature Reviewmentioning

confidence: 99%

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

et al. 2021

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“…A pooling-based deep recurrent neural network (DRNN) was proposed to learn the spatial information, which outperformed Support Vector regressor (SVR), Auto-Regressive Integrated Moving Average (ARIMA), and the classical deep recurrent neural network (RNN) [21]. In [22], Happy et al, proposed a statistical approach for load forecasting using quantile regression random forest, risk assessment index, and probability map. In [23], a backpropagation approach was utilized to perform short-term load forecasting utilizing weather data.…”

Section: Related Workmentioning

confidence: 99%

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark

et al. 2021

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Machine learning algorithms have been intensively applied to perform load forecasting to obtain better accuracies as compared to traditional statistical methods. However, with the huge increase in data size, sophisticated models have to be created which require big data platforms. Optimal and effective use of the available computational resources can be attained by maximizing the effective utilization of the cluster nodes. Parallel computing is demanded to allow for optimal resource utilization in dealing with smart grid big data. In this paper, a master-slave parallel computing paradigm is utilized and experimented with for load forecasting in a multi-AMI environment. The paper proposes a concurrent job scheduling algorithm in a multi-energy data source environment using Apache Spark. An efficient resource utilization strategy is proposed for submitting multiple Spark jobs to reduce job completion time. The optimal value of clustering is used in this paper to cluster the data into groups to be able to reduce the computational time additionally. Multiple tree-based machine learning algorithms are tested with parallel computation to evaluate the performance with tunable parameters on a real-world dataset. One thousand distribution transformers' real data from Spain for three years are used to demonstrate the performance of the proposed methodology with a trade-off between accuracy and processing time.INDEX TERMS Apache spark, concurrent computing, load forecasting, parallel processing, resource management.

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“…CNN structure and other supporting information are given in [17]. In order to verify the effectiveness of the deep learning-based ensemble uncertainty evaluator, the forecasting results are compared with the light gradient boosting machine (LGBM) [32], XGBOOST, CABOOST, Random Forest(RF) [33], Decision Tree(DT), BPNN [20], knearest neighbor (KNN), and SVR [18].…”

Section: A Experimental Settingsmentioning

confidence: 99%

Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

Qureshi¹,

Aziz²,

Bu³

et al. 2021

Preprint

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This paper presents a novel framework for cooperative trading in a price-maker wind power producer, that participates in the short-term electricity balance markets. In this framework, market price uncertainty is first modeled using a price uncertainty predictor, consisting of ridge regression (RR), nonpooling convolutional neural network (NPCNN), and linear quantile regression (LQR). RR is employed to select the correlated features to the corresponding forecast day, NPCNN is employed to extract the nonlinear features, and LQR is employed to estimate the price uncertainty. Then, an improved firefly algorithm (IFA) is proposed to solve the optimization problem. IFA uses the adaptive moment estimation method to improve the convergence speed and search for the global solution. Finally, the Shapley value is employed for the profit distribution of cooperative power producers. Illustrative examples show the effectiveness of the proposed framework and optimization model

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Statistical Load Forecasting Using Optimal Quantile Regression Random Forest and Risk Assessment Index

Cited by 81 publications

References 33 publications

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

A Combined Model Based on EOBL-CSSA-LSSVM for Power Load Forecasting

Distributed Tree-Based Machine Learning for Short-Term Load Forecasting With Apache Spark

Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty

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