Data-Driven Baseline Estimation of Residential Buildings for Demand Response †
Abstract
:1. Introduction
2. Data Mining Model
2.1. Data Preprocessing
2.2. Self-Organizing Map (SOM)
2.3. K-Means Clustering
2.4. Proposed Algorithm for Baseline Load Estimation
Algorithm 1 Baseline Load Estimation Algorithm |
|
Vector | Description |
---|---|
12 h consumption before DR activation | |
Average temperature | |
Gradient of the load consumption (optional) | |
Working day indicator (optional) |
3. Residential Demand Response (DR): Case Study
3.1. Load Characteristic
3.2. Data Processing
3.3. Determination of the Number of Clusters
3.3.1. The Dunn Index (DI)
3.3.2. The Davies-Bouldin Index (DBI)
3.3.3. The Silhouette Index (SI)
4. Experimental Results
4.1. Conventional Methods
4.2. Numerical Results
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
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Park, S.; Ryu, S.; Choi, Y.; Kim, J.; Kim, H. Data-Driven Baseline Estimation of Residential Buildings for Demand Response. Energies 2015, 8, 10239-10259. https://doi.org/10.3390/en80910239
Park S, Ryu S, Choi Y, Kim J, Kim H. Data-Driven Baseline Estimation of Residential Buildings for Demand Response. Energies. 2015; 8(9):10239-10259. https://doi.org/10.3390/en80910239
Chicago/Turabian StylePark, Saehong, Seunghyoung Ryu, Yohwan Choi, Jihyo Kim, and Hongseok Kim. 2015. "Data-Driven Baseline Estimation of Residential Buildings for Demand Response" Energies 8, no. 9: 10239-10259. https://doi.org/10.3390/en80910239