Abstract
Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without a priori classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015 |
| Publisher | Acta Press |
| Pages | 133-140 |
| Number of pages | 8 |
| ISBN (Electronic) | 9780889869776 |
| DOIs | |
| State | Published - 2015 |
| Event | 2015 IASTED International Symposium on Advances in Power and Energy Systems, APES 2015 - Marina del Rey, United States Duration: Oct 26 2015 → Oct 27 2015 |
Other
| Other | 2015 IASTED International Symposium on Advances in Power and Energy Systems, APES 2015 |
|---|---|
| Country | United States |
| City | Marina del Rey |
| Period | 10/26/15 → 10/27/15 |
Fingerprint
Keywords
- ANN
- Clustering
- Demand
- Framework
- Parameter estimation
- Pattern recognition
ASJC Scopus subject areas
- Fuel Technology
- Energy Engineering and Power Technology
Cite this
Electric load pattern classification for demand-side management planning : A hybrid approach. / Abdulaal, Ahmed; Buitrago, Jaime; Asfour, Shihab S.
Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015. Acta Press, 2015. p. 133-140.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Electric load pattern classification for demand-side management planning
T2 - A hybrid approach
AU - Abdulaal, Ahmed
AU - Buitrago, Jaime
AU - Asfour, Shihab S
PY - 2015
Y1 - 2015
N2 - Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without a priori classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.
AB - Smart Grids require a clear understanding of consumer demand patterns. Classification of consumers according to their demand pattern is required for the effective planning of tariffs, eligibility for demand-side management (DSM) programs, energy production and transmission, as well as for security purposes. We propose a framework for classification of consumer load patterns using a hybrid system with a parameter estimation model, a clustering model and an artificial neural network (ANN). The proposed model provides an effective unbiased classification method. The process starts with generating a training data set from existing consumers without a priori classification. The raw load data is processed through a parameter estimation model and a clustering algorithm to generate a training data set with distinct impartial classification clusters. The training data is fed to an ANN for learning. Once the load patterns are learned, the model can be used to further classify new consumers into a demand pattern. The ANN provides fast and accurate clustering performance without underlying assumptions about shape or class. An analysis of the optimal number of clusters is presented. Results indicate that clusters with distinguishable characteristics are achieved and we demonstrate how regulators can make use of this method in demand curtailment planning.
KW - ANN
KW - Clustering
KW - Demand
KW - Framework
KW - Parameter estimation
KW - Pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85015707491&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85015707491&partnerID=8YFLogxK
U2 - 10.2316/P.2015.831-012
DO - 10.2316/P.2015.831-012
M3 - Conference contribution
SP - 133
EP - 140
BT - Proceedings of the IASTED International Symposium on Advances in Power and Energy Systems, APES 2015
PB - Acta Press
ER -