Data-fusion for increasing temporal resolution of building energy management system data

Wijayasekara, Dumidu; Manic, Milos

doi:10.1109/iecon.2015.7392809

Cited by 16 publications

(9 citation statements)

References 26 publications

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“…Buildings are identified as a major energy consumer worldwide, accounting for 20%-40% of the total energy production [1]- [3]. In addition to being a major energy consumer, buildings are shown to account for a significant portion of energy wastage as well [4].…”

Section: Introductionmentioning

confidence: 99%

Building energy load forecasting using Deep Neural Networks

Marino¹,

Amarasinghe²,

Manic³

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

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455

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Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.

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Section: Introductionmentioning

confidence: 99%

Building energy load forecasting using Deep Neural Networks

Marino¹,

Amarasinghe²,

Manic³

2016

IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society

Self Cite

455

197

View full text Add to dashboard Cite

show abstract

“…Data association: represents data fusion schemes fusing data using the correlation between at least two or more information sources. Conventional schemes for data association incorporate KNN [49], probabilistic data association (PDA) [64,65], and multiple hypothesis test (MHT) [66,67]. • F2.…”

Section: Data Fusion Techniques (F)mentioning

confidence: 99%

“…Classification: indicates the strategy of clustering data into distinct categories using their unique characteristics. More discussion about generic classification approaches can be found in [44,66]. • F4.…”

Section: Data Fusion Techniques (F)mentioning

confidence: 99%

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

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Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

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“…However, the inherent uncertainties of the problem are ignored in their deterministic models, such as uncertainties of the thermal controlled loads (TCLs) and renewable resources, which are mainly determined by factors of weather and consumer behavior (Zhang et al 2018a). The uncertainties can be further intensified by missing samples and low resolution information in HVAC data collection (Wijayasekara andManic 2015, Žáčeková et al 2014). In this paper, we take the uncertainties into consideration by employing distributionally robust chanceconstrained (DRCC) programs.…”

Section: Introductionmentioning

confidence: 99%

Building Load Control using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Zhang

Dong

2021

Preprint

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Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. However, the time-varying PV generation is not perfectly known when the system operator decides the HVAC control schedules. To consider the unknown uncertain PV generation, in this paper, we formulate a distributionally robust chance-constrained (DRCC) building load control problem under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the DRCC problem under the Wasserstein ambiguity set, we utilize the righthand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve a trade-off between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions.

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Data-fusion for increasing temporal resolution of building energy management system data

Cited by 16 publications

References 26 publications

Building energy load forecasting using Deep Neural Networks

Building energy load forecasting using Deep Neural Networks

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Building Load Control using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

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