An open solution to provide personalized feedback for building energy management

Monacchi, Andrea; Versolatto, Fabio; Herold, Manuel; Egarter, Dominik; Tonello, Andrea M.; Elmenreich, Wilfried

doi:10.3233/ais-170422

Cited by 13 publications

(8 citation statements)

References 26 publications

(34 reference statements)

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“…www.nature.com/scientificdata www.nature.com/scientificdata/ a personalised feedback system 19 and a measurement campaign in Austrian households 20 . We define one uniform distribution per appliance based on those time windows.…”

Section: Dataset Generationmentioning

confidence: 99%

“…While some appliances such as dishwashers are designed to have clear programmes of operation with a predictable end time, it is challenging to identify the most appropriate power consumption pattern for user-controlled appliances such as hair dryers and microwave ovens. We addressed this issue by incorporating expert knowledge into our measurement campaign, which is a result of studies related to a personalised feedback system for energy management in households 19 and conclusions drawn from the outcome of a measurement campaign in Austrian households 20 . Based on this knowledge, we adapted the residents behaviour during measurements, i.e.…”

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confidence: 99%

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A synthetic energy dataset for non-intrusive load monitoring in households

et al. 2020

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Research on smart grid technologies is expected to result in effective climate change mitigation. Non-Intrusive Load Monitoring (NILM) is seen as a key technique for enabling innovative smart-grid services. By breaking down the energy consumption of households and industrial facilities into its components, NILM techniques provide information on present appliances and can be applied to perform diagnostics. As with related Machine Learning problems, research and development requires a sufficient amount of data to train and validate new approaches. As a viable alternative to collecting datasets in buildings during expensive and time-consuming measurement campaigns, the idea of generating synthetic datasets for NILM gain momentum recently. With SynD, we present a synthetic energy dataset with focus on residential buildings. We release 180 days of synthetic power data on aggregate level (i.e. mains) and individual appliances. SynD is the result of a custom simulation process that relies on power traces of real household appliances. In addition, we present several case studies that demonstrate similarity of our dataset and four real-world energy datasets. Background & Summary Load monitoring is vital for effective and accurate energy monitoring in buildings. Detailed insights can empower further research, help streamlining processes, and improve a building's energy efficiency 1. Introduced in 2 , Non-Intrusive Load Monitoring (NILM) techniques serve to break down a building's aggregate energy consumption to identify active appliances and also to provide diagnostic information. Extensive reviews can be obtained from 3 and 4. NILM can be considered as Machine Learning problem. As such, it requires datasets to train models, to conduct performance evaluation, to evaluate the benefit in real scenarios, and also to perform benchmarking on a common basis. In case of NILM, ground-truth data on aggregate and appliance-level energy consumption are crucial 4. Traditionally, NILM scholarship relies on energy consumption datasets. Such datasets usually contain information on energy consumption on aggregate level (monitored at the mains) and individual loads, which is provided by plug-level meters. Energy consumption datasets are the outcome of measurement campaigns in buildings or industrial facilities, which require expensive measurement equipment, bring bureaucratic burdens, and are time-consuming activities 5. As a viable alternative, the idea of generating synthetic data gain momentum recently. The main motivation behind generating synthetic datasets is to reduce costs for measurement campaigns and save valuable work hours. Instead, custom simulators provide energy consumption datasets on-demand and in contrast to real datasets, without limitations on measurement periods. Furthermore, real datasets suffer from missing readings (gaps), misaligned timestamps, and corrupted data as a result of sensor miscalculation or malfunction 6,7. Synthetic data does not show such issues. With SynD, we present a synthetic energy consumption da...

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Section: Dataset Generationmentioning

confidence: 99%

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A synthetic energy dataset for non-intrusive load monitoring in households

et al. 2020

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“…Further, management services are those that address specific functionalities of the smart home. Examples of such services can be those concerning the security and safety of inhabitants (for example the anti-intrusion alarms or the detection of gas leaks) [35] or those concerning energy efficiency of the home [65], such as those that control ventilation and solar panels for the energy production and those that control the appliances and lights to reduce energy consumption while satisfying the inhabitants' needs.…”

Section: Smart Homementioning

confidence: 99%

Internet of Things for enabling smart environments: A technology-centric perspective

Gómez

Chessa

Fleury

et al. 2019

AIS

114

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The Internet of Things (IoT) is a computing paradigm whereby everyday life objects are augmented with computational and wireless communication capabilities, typically through the incorporation of resource-constrained devices including sensors and actuators, which enable their connection to the Internet. The IoT is seen as the key ingredient for the development of smart environments. Nevertheless, the current IoT ecosystem offers many alternative communication solutions with diverse performance characteristics. This situation presents a major challenge to identifying the most suitable IoT communication solution(s) for a particular smart environment. In this paper we consider the distinct requirements of key smart environments, namely the smart home, smart health, smart cities and smart factories, and relate them to current IoT communication solutions. Specifically, we describe the core characteristics of these smart environments and then proceed to provide a comprehensive survey of relevant IoT communication technologies and architectures. We conclude with our reflections on the crucial features of IoT solutions in this setting and a discussion of challenges that remain open for research.

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“…In the study reported in Monacchi et al [16], we analyzed the GREEND dataset to identify energy hogs and possible policies for improving overall efficiency, which can be summarized as:…”

Section: Analyzing Energy Use and Improving Awarenessmentioning

confidence: 99%

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

Elmenreich¹,

Khatib²,

Monacchi³

2017

Research and Development Evolving Trends and Practices - Towards Human, Institutional and Economic Sectors Growth

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Smart microgrids are a possibility to reduce complexity by performing local optimization of power production, consumption and storage. We do not envision smart microgrids to be island solutions but rather to be integrated into a larger network of microgrids that form the future energy grid. Operating and controlling a smart microgrid involves optimization for using locally generated energy and to provide feedback to the user when and how to use devices. This chapter shows how these issues can be addressed starting with measuring and modeling energy consumption patterns by collecting an energy consumption dataset at device level. The open dataset allows to extract typical usage patterns and subsequently to model test scenarios for energy management algorithms. Section 3 discusses means for analyzing measured data and for providing detailed feedback about energy consumption to increase customers' energy awareness. Section 4 shows how renewable energy sources can be integrated in a smart microgrid and how energy production can be accurately predicted. Section 5 introduces a self-organizing local energy system that autonomously coordinates production and consumption via an agent-based energy auction system. The final section discusses how the proposed methods contribute to sustainable growth and gives an outlook to future research.

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An open solution to provide personalized feedback for building energy management

Cited by 13 publications

References 26 publications

A synthetic energy dataset for non-intrusive load monitoring in households

A synthetic energy dataset for non-intrusive load monitoring in households

Internet of Things for enabling smart environments: A technology-centric perspective

Smart Microgrids: Optimizing Local Resources toward Increased Efficiency and a More Sustainable Growth

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