Smart metering deployment status across EU-28

Bularca, Otilia; Florea, Monica; Dumitrescu, Ana-Maria

doi:10.1109/isfee.2018.8742468

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Thus, the buildings that offer demand response employing the flexibility of the corresponding HVAC load, as individual participants or aggregated with other entities, can become active bidders in the forthcoming flexible markets. Furthermore, the increasing penetration of smart meter readers across Europe (Bularca et al 2018), the growing number of smart/IoT devices, and the recent notable progress in the area of artificial intelligence are permitting the application of demand response strategies and forecasting the energy flexibility in buildings (Sharda et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Predicting HVAC-Based Demand Flexibility In Grid- Interactive Efficient Buildings Utilizing Deep Neural Networks

Avendano¹,

Moazami²,

Javan³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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Grid-interactive efficient buildings (GEBs) can provide flexibility services to the grid through demand response. This paper presents a novel predictive modeling methodology to estimate the availability of electrical demand flexibility in GEBs under demand response schemes. In this context, a physics-based energy simulation model of a reference building, considering the cooling demand in the summer season as the flexible load, is utilized. Accordingly, the impact of increasing the indoor setpoint temperature by 1.5 °C (for a maximum of 3 hours per day), which enables the demand side flexibility with a reduction of the cooling equipment’s electrical load, is simulated. Next, each demand response event is gathered, sorted, and then used to train the model to predict similar future events over the same time horizon in the following days. For this purpose, a deep neural network model trained using an expanding window training scheme is utilized to predict (15 minutes before the event) the load in the next 3 hours while undergoing the flexibility scenario. It is demonstrated that, with four months of training data, the model offers a promising prediction accuracy with a Mean Absolute Percentage Error (MAPE) of 3.55%.

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Section: Introductionmentioning

confidence: 99%

Predicting HVAC-Based Demand Flexibility In Grid- Interactive Efficient Buildings Utilizing Deep Neural Networks

Avendano¹,

Moazami²,

Javan³

et al. 2023

ECMS 2023 Proceedings Edited by Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni

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show abstract

“…and continued installing at the consumers with a target to install about 35.9 million new SMs by 2031 [3]. Nevertheless, the use of high-resolution SM always risks the privacy of energy consumers [4].…”

Section: Introductionmentioning

confidence: 99%

Practically Private Smart Meters with Energy Storage: An Experience-Driven Approach

Pokhrel¹,

Hossain²

2022

Preprint

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“…According to a 2014 report on progress in smart meter deployment, issued by the EC, it is expected that almost 72% of European consumers will have a smart meter for electricity by 2020 (European Commission 2014a). However, delays in starting the deployment, a lack of a legal framework, late approval of rollout plans and political and/or financial instability across Europe call into question the 72% expectation (Bularca et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Smart Meter Awareness in Italy, Ancona

Ntouros

Kampelis

Senzacqua

et al. 2021

Smart and Sustainable Planning for Cities and Regions

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Smart meters, one of the crucial enablers of the smart-grid concept and cornerstones in smart planning for cities, offer the opportunity for consumers to address their energy consumption effectively through timely and accurate data on their energy usage. However, previous studies have shown that smart meters may not lead to the desired energy savings unless actively used by households. To this end, the research presented in this paper investigates the penetration of smart meters at community level and explores how such a metering system can help people to understand and manage their energy use better. It examines the awareness about smart meters, looks into their presence in current accommodation and focuses on the views people have about smart meters. For this purpose, a questionnaire was prepared and distributed to a group of individuals residing in the wide area of Ancona province in Italy. Although the deployment of modern second-generation smart meters started in 2017 replacing the outdated smart meters massively installed in the 2000s, the results show low-to-moderate levels of awareness of modern smart meters among the respondents and a low presence of second-generation metering devices in their current accommodation. However, the general view expressed by the participants about smart meters is positive. The findings demonstrate that respondents are in need not only of a gauge that measures energy consumption but also of a tool that assists them to manage effectively their energy use.

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Smart metering deployment status across EU-28

Cited by 4 publications

References 3 publications

Predicting HVAC-Based Demand Flexibility In Grid- Interactive Efficient Buildings Utilizing Deep Neural Networks

Predicting HVAC-Based Demand Flexibility In Grid- Interactive Efficient Buildings Utilizing Deep Neural Networks

Practically Private Smart Meters with Energy Storage: An Experience-Driven Approach

Smart Meter Awareness in Italy, Ancona

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