Optimal combined short-term building load forecasting

Borges, Cruz E.; Penya, Yoseba K.; Fernández, Iván

doi:10.1109/isgt-asia.2011.6167091

Cited by 26 publications

(16 citation statements)

References 38 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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.…”

Section: Proposed Post-process Methodsmentioning

confidence: 99%

“…We have previously researched on this field [13]. Nevertheless, it was not focused on the robustness of the prediction but on the evaluation of model combination and other model-selection methods.…”

Section: Related Workmentioning

confidence: 99%

“…-Model Combination: another option is to group all the predictions issued by the forecasters in order to build a more robust forecast. We have addressed this strategy in [13,32].…”

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

“…-Model Selection: some models issue a more reliable forecast at certain hours or day-types than others. In [13], we presented a comparison of different strategies to select the best model in every moment. -Model Combination: another option is to group all the predictions issued by the forecasters in order to build a more robust forecast.…”

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

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

et al. 2013

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Proposed Post-process Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…-Model Combination: another option is to group all the predictions issued by the forecasters in order to build a more robust forecast. We have addressed this strategy in [13,32].…”

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

Section: Data Distribution Service (Dds)mentioning

confidence: 99%

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

et al. 2013

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Demand Forecasting in Smart Grids

Mirowski¹,

Sining²,

Ho³

et al. 2014

Bell Labs Tech. J.

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Data analytics in smart grids can be leveraged to channel the data downpour from individual meters into knowledge valuable to electric power utilities and end‐consumers. Short‐term load forecasting (STLF) can address issues vital to a utility but it has traditionally been done mostly at system (city or country) level. In this case study, we exploit rich, multi‐year, and high‐frequency annotated data collected via a metering infrastructure to perform STLF on aggregates of power meters in a mid‐sized city. For smart meter aggregates complemented with geo‐specific weather data, we benchmark several state‐of‐the‐art forecasting algorithms, including kernel methods for nonlinear regression, seasonal and temperature‐adjusted auto‐regressive models, exponential smoothing and state‐space models. We show how STLF accuracy improves at larger meter aggregation (at feeder, substation, and system‐wide level). We provide an overview of our algorithms for load prediction and discuss system performance issues that impact real time STLF. © 2014 Alcatel‐Lucent.

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