Electricity load demand forecasting in Portugal using least-squares support vec...

• Autor(es): Cuambe, Isaura Denise Filipe
• Data: 2013
• Identificador Persistente: http://hdl.handle.net/10400.1/3553
• Origem: Sapientia - Universidade do Algarve
• Assunto(s): Engenharia informática; Energia eléctrica; Produção de energia; Consumo de energia
• Tipo de Documento: Dissertação de Mestrado
• Idioma: Inglês
• Orientador(es): Ferreira, P. M.

Descrição

Dissertação de mest., Engenharia Informática, Faculdade de Ciências e Tecnologia, Univ. do Algarve, 2013 Electricity Load Demand (ELD) forecasting is a subject that is of interest mainly to producers and distributors and it has a great impact on the national economy. At the national scale it is not viable to store electricity and it is also difficult to estimate its consumption accurately enough in order to provide a better agreement between supply and demand and consequently less waste of energy. Thus, researchers from many areas have addressed this issue in a way to facilitate the task of power grid companies in adjusting production levels to consumption demand. Over the years, many predictive algorithms were tested and the Radial Basis Function Artificial Neural Network (RBF ANN) was up to now one of the most tested approaches with satisfactory results. The fact that the on-line adaptation is not an easy task for this approach, led demand for new ways to make the prediction, promising better results, or at least as good as those of RBF ANN, and also the ability to overcome the difficulties founded by RBF ANN in on-line adaptation. This work aims at introducing a new approach still little explored for electricity consumption prediction. Least-Squares Support Vector Machines (LS-SVMs) are a good alternative to RBF ANN and other approaches, since they have fewer parameters to adjust, hence, allowing significant decrease in the sensitivity of those machines to well-known problems associated with parameter adaptation, making the on-line model adaptation more stable over time