Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment

Larsen, Emil Mahler; Leimgruber, F.; Judex, Florian

doi:10.1016/j.segan.2017.03.001

Cited by 30 publications

(14 citation statements)

References 22 publications

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“…Using this method, Larsen et al [12] and [10], shows that it is possible to extract FIRs to the real time market from another period of the Ecogrid EU data. With prices, weather and a set of Fourier terms as input to model the general behaviour and a difference model modelling the response from changes in the pricing.…”

Section: Methods and Datasetmentioning

confidence: 99%

Residential demand response behaviour modeling applied to cyber-physical intrusion detection

Heussen

Tyge

Kosek

2017

2017 IEEE Manchester PowerTech

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Abstract-A real-time demand response system can be viewed as a cyber-physical system, with physical systems dependent on cyber infrastructure for coordination and control, which may be vulnerable to cyber-attacks. The time domain dynamic behaviour of individual residential demand responses is governed by a mix of physical system parameters, exogenous influences, user behaviour and preferences, which can be characterized by unstructured models such as a time-varying finite impulse response. In this study, which is based on field data, it is shown how this characteristic response behaviours can be identified and how the characterization can be updated continuously. Finally, we propose an approach to apply this behaviour characterization to the identification of anomalous and potentially malicious behaviour modifications as part of a cyber-physical intrusion detection mechanism.

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Section: Methods and Datasetmentioning

confidence: 99%

Residential demand response behaviour modeling applied to cyber-physical intrusion detection

Heussen

Tyge

Kosek

2017

2017 IEEE Manchester PowerTech

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“…Therefore, evaluation of the estimations is kept independent by using the Mean error that can be used to compare uncertainty across the four estimation methods. They are also deemed suitable based on their general usage across the literature on DSR estimation methods (Aman et al, 2016;Larsen et al, 2015).…”

Section: Comparison Approachmentioning

confidence: 99%

A comparative analysis of building energy estimation methods in the context of demand response

Curtis

Torriti

Smith

2018

Energy and Buildings

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A critical element of assessing a building's suitability for Demand Side Response (DSR) is understanding Highlights  Four DSR estimation methods were evaluated using empirical data from two hotels  The DSR estimation methods were found to have a wide range of error levels  The comparisons of methods allows for informed selection of a DSR estimation method based on available input information

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“…There were four groups that were fitted with automated equipment (groups 3-6). group 1 was designed as a reference group, but was shown to be non representative when attempting to generate a reliable baseline [9]. group 2 was a manual group with no automation of devices and participants were asked to modify their consumption through SMS.…”

Section: Electric Load Data Collectionmentioning

confidence: 99%

“…The response in each cluster, is estimated using Finite Impulse Responses (FIR) for price [9]. FIRs are obtained from a general linear model of x t the observed load,…”

Section: B Response Quantificationmentioning

confidence: 99%

Data-driven demand response characterization and quantification

Ray

Larsen

2017

2017 IEEE Manchester PowerTech

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Abstract-Analysis of load behavior in demand response (DR) schemes is important to evaluate the performance of participants. Very few real-world experiments have been carried out and quantification and characterization of the response is a difficult task. Nevertheless it will be a necessary tool for portfolio management of consumers in a DR framework. In this paper we develop methods to quantify and characterize the amount of DR in a load. The contribution to the aggregated load from each household is quantified on a daily basis, showing the potential variability of the response in time. Clustering on the average values and standard deviation of the contribution regroups households with the same average response. Independent Component Analysis (ICA) is used to characterize different DR delivery profiles.

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Demand response evaluation and forecasting — Methods and results from the EcoGrid EU experiment

Cited by 30 publications

References 22 publications

Residential demand response behaviour modeling applied to cyber-physical intrusion detection

Residential demand response behaviour modeling applied to cyber-physical intrusion detection

A comparative analysis of building energy estimation methods in the context of demand response

Data-driven demand response characterization and quantification

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