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Short-term load forecasting using time series clustering
https://orcid.org/http://orcid.org/0000-0003-3733-6619
https://orcid.org/http://orcid.org/0000-0002-7047-6210
https://orcid.org/http://orcid.org/0000-0001-6239-7283
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.
Short-term load forecasting using time series clustering
https://orcid.org/http://orcid.org/0000-0003-3733-6619
https://orcid.org/http://orcid.org/0000-0002-7047-6210
https://orcid.org/http://orcid.org/0000-0001-6239-7283
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.
Short-term load forecasting using time series clustering
https://orcid.org/http://orcid.org/0000-0003-3733-6619
https://orcid.org/http://orcid.org/0000-0002-7047-6210
https://orcid.org/http://orcid.org/0000-0001-6239-7283
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.
Short-term load forecasting using time series clustering
Optim Eng
Martins, Ana (author) / Lagarto, João (author) / Canacsinh, Hiren (author) / Reis, Francisco (author) / Cardoso, Margarida G. M. S. (author)
Optimization and Engineering ; 23 ; 2293-2314
2022-12-01
22 pages
Article (Journal)
Electronic Resource
English
Clustering time series , Distance measures , Load pattern , Sequence Pattern , Similar Pattern Method , Short-term load forecasting Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operations Research/Decision Theory , Financial Engineering , Mathematics and Statistics
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