TY - GEN
T1 - An ensemble model for day-ahead electricity demand time series forecasting
AU - Shen, Wen
AU - Babushkin, Vahan
AU - Aung, Zeyar
AU - Woon, Wei Lee
PY - 2013
Y1 - 2013
N2 - In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.
AB - In this work, we try to solve the problem of day-ahead prediction of electricity demand using an ensemble forecasting model. Based on the Pattern Sequence Similarity (PSF) algorithm, we implemented five forecasting models using different clustering techniques: K-means model (as in original PSF), Self-Organizing Map model, Hierarchical Clustering model, K-medoids model, and Fuzzy C-means model. By incorporating these five models, we then proposed an ensemble model, named Pattern Forecasting Ensemble Model (PFEM), with iterative prediction procedure. We evaluated its performance on three real-world electricity demand datasets and compared it with those of the five forecasting models individually. Experimental results show that PFEM outperforms all those five individual models in terms of Mean Error Relative and Mean Absolute Error.
KW - clustering
KW - ensemble model
KW - time series forecasting
UR - http://www.scopus.com/inward/record.url?scp=84878634819&partnerID=8YFLogxK
U2 - 10.1145/2487166.2487173
DO - 10.1145/2487166.2487173
M3 - Conference contribution
AN - SCOPUS:84878634819
SN - 9781450320528
T3 - e-Energy 2013 - Proceedings of the 4th ACM International Conference on Future Energy Systems
SP - 51
EP - 62
BT - e-Energy 2013 - Proceedings of the 4th ACM International Conference on Future Energy Systems
T2 - 4th ACM International Conference on Future Energy Systems, e-Energy 2013
Y2 - 21 May 2013 through 24 May 2013
ER -