Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description

Wang, Fei; Li, Kangping; Li, Chun; Mi, Zengqiang; Shafie‐khah, Miadreza

doi:10.1109/tsg.2018.2824842

Cited by 170 publications

(56 citation statements)

References 19 publications

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“…K-means, self-organizing maps (SOM), mixture models, expectation maximization (EM), and spectral clustering have been widely used as clustering methods. Among the several methods available for clustering to address load pattern segmentation, the most commonly employed are standard k-means [5][6][7][8][9][10], adaptive k-means [11,12], fuzzy k-means [13,14], and g-means [15], which is an alternative clustering model to k-means. SOM [16,17] is commonly employed by itself but has also been combined with other clustering methods such as k-means and hierarchical clustering as a hybrid model [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

2020

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Due to the heterogeneity of demand response behaviors among customers, selecting a suitable segment is one of the key factors for the efficient and stable operation of the demand response (DR) program. Most utilities recognize the importance of targeted enrollment. Customer targeting in DR programs is normally implemented based on customer segmentation. Residential customers are characterized by low electricity consumption and large variability across times of consumption. These factors are considered to be the primary challenges in household load profile segmentation. Existing customer segmentation methods have limitations in reflecting daily consumption of electricity, peak demand timings, and load patterns. In this study, we propose a new clustering method to segment customers more effectively in residential demand response programs and thereby, identify suitable customer targets in DR. The approach can be described as a two-stage k-means procedure including consumption features and load patterns. We provide evidence of the outstanding performance of the proposed method compared to existing k-means, Self-Organizing Map (SOM) and Fuzzy C-Means (FCM) models. Segmentation results are also analyzed to identify appropriate groups participating in DR, and the DR effect of targeted groups was estimated in comparison with customers without load profile segmentation. We applied the proposed method to residential customers who participated in a peak-time rebate pilot DR program in Korea. The result proves that the proposed method shows outstanding performance: demand reduction increased by 33.44% compared with the opt-in case and the utility saving cost in DR operation was 437,256 KRW. Furthermore, our study shows that organizations applying DR programs, such as retail utilities or independent system operators, can more economically manage incentive-based DR programs by selecting targeted customers.

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Section: Literature Reviewmentioning

confidence: 99%

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

2020

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“…Then, a smoothing spline is fitted to create the baseline. A similar approach, using the consumption of non-DR participants to estimate the baseline of DR participants is proposed in [33] and in [34]. The problem with this practical approach is that always a reference population with similar characteristics needs to exist, which does not participate in DR activations.…”

Section: Baselinesmentioning

confidence: 99%

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

et al. 2019

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“…And irrational DR programs will destructive the balance between people's comfortable degree and willingness degree of energy saving. In addition, the knowledge of residential electricity consumption behavior is helpful for the system operators (e.g., transmission system operators and distribution system operators) to achieve the reliable smart residential energy scheduling [48][49][50].…”

Section: Applicationmentioning

confidence: 99%

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

Wang

et al. 2018

Energies

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This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

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Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description

Cited by 170 publications

References 19 publications

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study

Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data

Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

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