An Ensemble Forecasting Method for the Aggregated Load With Subprofiles

Wang, Yi; Chen, Qixin; Sun, Mingyang; Chen, Kang; Xia, Qing

doi:10.1109/tsg.2018.2807985

Cited by 181 publications

(65 citation statements)

References 6 publications

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“…average, 9,10 wavelet analysis, 11,12 and so on; intelligent methods, such as support vector machine (SVM), 13,14 random forest, [15][16][17] neural networks, [18][19][20] and other intelligent forecasting methods. Other methods, such as hybrid technology, [21][22][23][24] are a combination of more than one technology, that is, a combination of traditional and intelligent method technologies or a combination of different intelligent methods. Among these intelligent load forecasting methods, artificial neural network method is the most widely used model.…”

Section: Auto-regressive Movingmentioning

confidence: 99%

Short‐term load forecasting method based on deep neural network with sample weights

Cai

Yan

et al. 2020

Int Trans Electr Energ Syst

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Summary The reform of power market has presented new challenges to short‐term load forecasting (STLF), and the accuracy of forecast results is of great significance to the orderly and efficient operation of the market. To improve the accuracy of load forecasting, an STLF method based on deep neural network (DNN) with sample weights is proposed. By filtering samples and assigning corresponding weights to different training samples, this method effectively improves the forecasting accuracy of the DNN model. Two important steps of this method are as follows: (a) calculating the distance between the samples and the forecast day to measure the similarity between them, and the training samples are selected based on the distance; (b) after selecting training samples, corresponding weights can be assigned to training samples according to the distance between the training samples and the forecast day. This allows DNN to focus on the key samples. Finally, we carried out simulation analysis by using the actual load data of Guangdong Power Grid in January 2017. The results show that the proposed method can effectively improve the accuracy and reliability of load forecasting.

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Section: Auto-regressive Movingmentioning

confidence: 99%

Short‐term load forecasting method based on deep neural network with sample weights

Cai

Yan

et al. 2020

Int Trans Electr Energ Syst

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“…The data-mining model for CBL calculation is based on clustering. Clustering is a useful tool for data analysis and it has been widely used in the study of the modern power system, such as electrical consumption analysis [7] and electrical load prediction [16,17]. Various clustering algorithms have been studied in previous work, including hierarchical clustering, affinity propagation clustering, and fuzzy c-means clustering.…”

Section: Data-mining Approach Based On Clustering Analysismentioning

confidence: 99%

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Song

Zhang

et al. 2018

Energies

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Demand response (DR) in the wholesale electricity market provides an economical and efficient way for customers to participate in the trade during the DR event period. There are various methods to measure the performance of a DR program, among which customer baseline load (CBL) is the most important method in this regard. It provides a prediction of counterfactual consumption levels that customer load would have been without a DR program. Actually, it is an expected load profile. Since the calculation of CBL should be fair and simple, the typical methods that are based on the average model and regression model are the two widely used methods. In this paper, a cluster-based approach is proposed considering the multiple power usage patterns of an individual customer throughout the year. It divides loads of a customer into different types of power usage patterns and it implicitly incorporates the impact of weather and holiday into the CBL calculation. As a result, different baseline calculation approaches could be applied to each customer according to the type of his power usage patterns. Finally, several case studies are conducted on the actual utility meter data, through which the effectiveness of the proposed CBL calculation approach is verified.

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“…For retailers, residential load forecasting serves for pricing, purchasing, and hedging decisions and maximizes retailers' profits [6]. To aggregators, it is utilized to produce more accurate aggregate load forecasts by clustering or other methods [7,8]. In distribution system operators (DSO), depending on effective residential load forecasting, peak load reduction can be achieved by flexible use of the energy storage (ES) system or intelligent demand response (DR) technology.…”

Section: Introductionmentioning

confidence: 99%

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

et al. 2020

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Residential load forecasting is important for many entities in the electricity market, but the load profile of single residence shows more volatilities and uncertainties. Due to the difficulty in producing reliable point forecasts, probabilistic load forecasting becomes more popular as a result of catching the volatility and uncertainty by intervals, density, or quantiles. In this paper, we propose a unified quantile regression deep neural network with time-cognition for tackling this challenging issue. At first, a convolutional neural network with multiscale convolution is devised for extracting more behavioral features from the historical load sequence. In addition, a novel periodical coding method marks the model to enhance its ability of capturing regular load pattern. Then, features generated from both subnetworks are fused and fed into the forecasting model with an end-to-end manner. Besides, a globally differentiable quantile loss function constrains the whole network for training. At last, forecasts of multiple quantiles are directly generated in one shot. With ablation experiments, the proposed model achieved the best results in the AQS, AACE, and inversion error, and especially the average of the AACE is grown by 34.71%, 75.22%, and 32.44% compared with QGBRT, QCNN, and QLSTM, respectively, indicating that our method has excellent reliability and robustness rather than the state-of-the-art models obviously. Meanwhile, great performances of efficient time response demonstrate that our proposed work has promising prospects in practical applications.

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An Ensemble Forecasting Method for the Aggregated Load With Subprofiles

Cited by 181 publications

References 6 publications

Short‐term load forecasting method based on deep neural network with sample weights

Short‐term load forecasting method based on deep neural network with sample weights

A Cluster-Based Baseline Load Calculation Approach for Individual Industrial and Commercial Customer

Unified Quantile Regression Deep Neural Network with Time-Cognition for Probabilistic Residential Load Forecasting

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