Electricity load modelling using computational intelligence

Electricity load modelling using computational intelligence

Ter Borg, R.W.

Koppelaar, H. (promotor)
Rothkrantz, L.J.M. (promotor)

Electrical Engineering, Mathematics and Computer Science

2005-12-14

As a consequence of the liberalisation of the electricity markets in Europe, market players have to continuously adapt their future supply to match their customers' demands. This poses the challenge of obtaining a predictive model that accurately describes electricity loads, current in this thesis. Kernel machines are considered to be the state of the art of supervised learning methods. A Bayesian-framework based kernel-machine is extended to represent data in a way that is sparse in feature space and smooth in output space. It is argued that this leads to a higher degree of generalisation. Kernel machines can be tailored to better suit one's demands; electricity-demand-specific representations are designed for day types and for emphasising twilight periods. A multi-component setup is proposed to increase the orthogonality between input variables. For wind-power production forecasting, data from several weather stations is combined to refine the coarse resolution of wind-speed measurements. To put theory into practice, the kernel-machine library has been developed. It offers its users efficiency and flexibility. All proposed representations are implemented and tested for their embeddability in a real-world environment. The multi-component structureis filled with calendar, trend, temperature, radiation, and wind components. These components enable the electricity demands to be unravelled; several new explicit facts are discovered, such as the influence of Sinterklaas and a cloudburst. The resulting systems produce competitively accurate and detailed predictions of past and future electricity loads.

electricity load
wind energy
Kernel machines

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Institutional Repository

doctoral thesis

(c) 2005 R.W. ter Borg