Short-term load forecasting using time series clustering

Short-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering...

Full description

Saved in:
Bibliographic Details
Published inOptimization and engineering Vol. 23; no. 4; pp. 2293 - 2314
Main Authors Martins, Ana, Lagarto, João, Canacsinh, Hiren, Reis, Francisco, Cardoso, Margarida G. M. S.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.12.2022
Springer Nature B.V
Subjects
Algorithms
Clustering
Control
Engineering
Environmental Management
Financial Engineering
Forecasting
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Research Article
Sequences
Systems Theory
Time series
Load pattern
Short-term load forecasting
Clustering time series
Distance measures
Sequence Pattern
Similar Pattern Method
Online AccessGet full text

Cover

More Information
Summary:Short-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering algorithm to identify load patterns and to the COMB distance to capture differences between time series. Clusters’ labels are then used to identify similar sequences of days. Temperature information is also considered in the day-ahead load forecasting, resorting to the K-Nearest Neighbor approach. CSPF algorithm is intended to provide the aggregate forecast of Portugal's national load, for the next day, with a 15-min discretization, based on data from the Portuguese Transport Network Operator (TSO). CSPF forecasting performance, as evaluated by RMSE, MAE and MAPE metrics, outperforms three alternative/baseline methods, suggesting that the proposed approach is promising in similar applications.
ISSN:1389-4420
1573-2924
DOI:10.1007/s11081-022-09760-1