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Iván Fernandez
DeustoTech-Deusto Technology Foundation, Energy Unit, University of Deusto, Avenida de las Universidades 24, Bilbao 48007, Basque Country, Spain

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Journal article
Published: 17 April 2013 in Energies
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Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.

ACS Style

Cruz E. Borges; Yoseba K. Penya; Iván Fernandez; Juan Prieto; Oscar Bretos. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. Energies 2013, 6, 2110 -2129.

AMA Style

Cruz E. Borges, Yoseba K. Penya, Iván Fernandez, Juan Prieto, Oscar Bretos. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. Energies. 2013; 6 (4):2110-2129.

Chicago/Turabian Style

Cruz E. Borges; Yoseba K. Penya; Iván Fernandez; Juan Prieto; Oscar Bretos. 2013. "Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings." Energies 6, no. 4: 2110-2129.

Journal article
Published: 19 September 2012 in IEEE Transactions on Industrial Informatics
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We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows us to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids.

ACS Style

Cruz Enrique Borges; Yoseba K. Penya; Ivan Fernandez. Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids. IEEE Transactions on Industrial Informatics 2012, 9, 1570 -1577.

AMA Style

Cruz Enrique Borges, Yoseba K. Penya, Ivan Fernandez. Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids. IEEE Transactions on Industrial Informatics. 2012; 9 (3):1570-1577.

Chicago/Turabian Style

Cruz Enrique Borges; Yoseba K. Penya; Ivan Fernandez. 2012. "Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids." IEEE Transactions on Industrial Informatics 9, no. 3: 1570-1577.