Efficient building load forecasting

Abstract

Abstract-The arrival of the smart grid paradigm has brought a number of novel initiatives that aim at increasing the level of energy efficiency of buildings such as smart metering or demand side management. Still, all of them demand an accurate load estimation. Short-term load forecasting in buildings presents additional requirements, among others the need of prediction models with simple or non-existing parametrisation processes. We extend a previous work that evaluated a number of algorithms to this end. Herewith we present several improvements including a variable data learning window and diverse learning data weighting combinations that further up improve our results. Finally, we have tested all the algorithms and modalities with four different datasets to show how the results hold up.

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

“…Please note that this format has been chosen to assess the suitability of the post-process methods defined in Section 3.2 with the two proposed forecasting algorithms, not to compare the AR and SVM models (for such a comparison we refer the readers to [10,13,30,32]). Figure 7 shows the percentage of days detected as anomalous by the AR and SVM model.…”

“…Since the learning windows are very short (just three days in this test, see [10,13] for a broad comparison in this matter) and the models used are sufficiently robust to tailor this degenerated training set, we are able to issue a new forecast in this situation. Moreover, the forecast produced is the obvious one, just the values observed the previous day (i.e., acts like a Random Walk Model), so this adjustment quickly adapts to changes in the dataset.…”

“…Note that in this paper we will only use SVM since we have not been able to replicate the results from the literature involving NNs. Moreover, our tests have proven that NNs are slower and do not obtain better results than other techniques (see for example [10]). …”

“…In this work, we have used AR and SVM models, since according to our experiments [10,30] they produce the best results using this methodology. Moreover, in these works we have optimised the free parameters by means of a grid search following the advice given in [31] and we used the results of these test here.…”

“…We can measure that bias and compensate it. In [10] we have assessed the performance of this post-process. -Model Selection: some models issue a more reliable forecast at certain hours or day-types than others.…”