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Electricity load demand forecasting in Portugal using least-squares support vector machines

Publication . Cuambe, Isaura Denise Filipe; Ferreira, P. M.

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

2013Master thesis

Open access

Correction: Ferreira, P.M., et al. A neural network based intelligent predictive sensor for cloudiness, solar radiation and air temperature. Sensors 2012, 12, 15750–15777

Publication . Ferreira, P. M.; Gomes, João M.; Martins, I.; Ruano, Antonio

Accurate measurements of global solar radiation and atmospheric temperature, as well as the availability of the predictions of their evolution over time, are important for different areas of applications, such as agriculture, renewable energy and energy management, or thermal comfort in buildings. For this reason, an intelligent, light-weight and portable sensor was developed, using artificial neural network models as the time-series predictor mechanisms. These have been identified with the aid of a procedure based on the multi-objective genetic algorithm. As cloudiness is the most significant factor affecting the solar radiation reaching a particular location on the Earth surface, it has great impact on the performance of predictive solar radiation models for that location. This work also represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate cloudiness by the fraction of visible sky corresponding to clouds and to clear sky. The implementation of predictive models in the prototype has been validated and the system is able to function reliably, providing measurements and four-hour forecasts of cloudiness, solar radiation and air temperature.

2013Journal article

Open access