In this paper we present a new approach for time series forecasting, called Maximum Length Weighted Nearest Neighbor (MLWNN), which combines prediction based on sequence similarity with optimization techniques. MLWNN predicts the 24 hourly electricity loads for the next day, from a time sequence of previously electricity loads up to the current day. We evaluate MLWNN using electricity load data for two years, for three countries (Australia, Portugal and Spain), and compare its performance with three state-of-the-art methods (weighted nearest neighbor, pattern sequence-based forecasting and iterative neural network) and with two baselines. The results show that MLWNN is a promising approach for one day ahead electricity load forecasting.

Maximum Length Weighted Nearest Neighbor Approach for Electricity Load Forecasting / Colombo, T.; Koprinska, I.; Panella, Massimo. - STAMPA. - (2015), pp. 3751-3758. (Intervento presentato al convegno International Joint Conference on Neural Networks (IJCNN 2015) tenutosi a Killarney, Repubblica di Irlanda nel 12-17 luglio 2015) [10.1109/IJCNN.2015.7280809].

Maximum Length Weighted Nearest Neighbor Approach for Electricity Load Forecasting

Colombo, T.;PANELLA, Massimo
2015

Abstract

In this paper we present a new approach for time series forecasting, called Maximum Length Weighted Nearest Neighbor (MLWNN), which combines prediction based on sequence similarity with optimization techniques. MLWNN predicts the 24 hourly electricity loads for the next day, from a time sequence of previously electricity loads up to the current day. We evaluate MLWNN using electricity load data for two years, for three countries (Australia, Portugal and Spain), and compare its performance with three state-of-the-art methods (weighted nearest neighbor, pattern sequence-based forecasting and iterative neural network) and with two baselines. The results show that MLWNN is a promising approach for one day ahead electricity load forecasting.
2015
International Joint Conference on Neural Networks (IJCNN 2015)
neural networks; forecasting
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Maximum Length Weighted Nearest Neighbor Approach for Electricity Load Forecasting / Colombo, T.; Koprinska, I.; Panella, Massimo. - STAMPA. - (2015), pp. 3751-3758. (Intervento presentato al convegno International Joint Conference on Neural Networks (IJCNN 2015) tenutosi a Killarney, Repubblica di Irlanda nel 12-17 luglio 2015) [10.1109/IJCNN.2015.7280809].
File allegati a questo prodotto
File Dimensione Formato  
Dichiarazione_conformità 18-11-2016.pdf

solo utenti autorizzati

Tipologia: Altro materiale allegato
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 1.98 MB
Formato Adobe PDF
1.98 MB Adobe PDF   Contatta l'autore
Colombo_Maximum-length_2016.pdf

solo utenti autorizzati

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.02 MB
Formato Adobe PDF
2.02 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/778365
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 0
social impact