Intelligent Infrastructure for Energy Efficiency

Gershenfeld, Neil; Samouhos, Stephen; Nordman, Bruce

doi:10.1126/science.1174082

Cited by 60 publications

(25 citation statements)

References 7 publications

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“…A building's ability to reduce overall energy use and peak demand may be substantial, depending on the systems in place and data available to inform decisions, and tuning building power draw in response to utility signals and other inputs may be one element of the Smart Grid [1]. As part of this strategy a building operator may wish to explore and manipulate building energy use a few hours ahead; actions might involve load shedding, pre-cooling, charging of ice storage, activation of local generation, or a variety of other actions [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…In recent decades a class of time series analysis models named ARIMAX (Auto Regressive Integrated Moving Average with eXternal (or eXogenous) input) has been developed for forecasting in other domains, particularly in economics [7]. The "integrated" part of the name indicates that it is often required that one runs the analysis on the change in the dependent variable of interest (known as "differencing"), to render the series stationary 1 . "Auto regressive" indicates that the forecasted value of the dependent variable may be predicted from prior, known, values of the dependent variable.…”

Section: Introductionmentioning

confidence: 99%

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Building-level occupancy data to improve ARIMA-based electricity use forecasts

Newsham

Birt

2010

Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building

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The material in this document is covered by the provisions of the Copyright Act, by Canadian laws, policies, regulations and international agreements. Such provisions serve to identify the information source and, in specific instances, to prohibit reproduction of materials without written permission. For more information visit http://laws.justice.gc.ca/en/showtdm/cs/C-42Les renseignements dans ce document sont protégés par la Loi sur le droit d'auteur, par les lois, les politiques et les règlements du Canada et des accords internationaux. Ces dispositions permettent d'identifier la source de l'information et, dans certains cas, d'interdire la copie de documents sans permission écrite. Pour obtenir de plus amples renseignements : http://lois.justice.gc.ca/fr/showtdm/cs/C-42 benjamin.birt@nrc-cnrc.gc.ca ABSTRACTThe energy use of an office building is likely to correlate with the number of occupants, and thus knowing occupancy levels should improve energy use forecasts.To gather data related to total building occupancy, wireless sensors were installed in a three-storey building in eastern Ontario, Canada comprising laboratories and 81 individual work spaces. Contact closure sensors were placed on various doors, PIR motion sensors were placed in the main corridor on each floor, and a carbon-dioxide sensor was positioned in a circulation area. In addition, we collected data on the number of people who had logged in to the network on each day, network activity, electrical energy use (total building, and chilling plant only), and outdoor temperature. We developed an ARIMAX model to forecast the power demand of the building in which a measure of building occupancy was a significant independent variable and increased the model accuracy. The results are promising, and suggest that further work on a larger and more typical office building would be beneficial. If building operators have a tool that can accurately forecast the energy use of their building several hours ahead they can better respond to utility price signals, and play a fuller role in the coming Smart Grid.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building-level occupancy data to improve ARIMA-based electricity use forecasts

Newsham

Birt

2010

Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building

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“…One of the main tasks in building automation is the efficient use of energy resources due to the high building consumption, which is around 70% of the electricity produced in developed countries [51]. In particular, lighting systems account for a significant energy usage with a 25% and a 12% in commercial and residential sectors, respectively [52], representing about 7% of the global CO 2 emissions by the flaring of fossil fuels to produce electricity [53].…”

Section: Engineering Applications Lighting Systemsmentioning

confidence: 99%

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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Recently, there has been an increasing interest in studying large-scale distributed systems in the control community. Efforts have been invested in developing several techniques wishing to address the main challenges found in this kind of problems, for instance, the amount of information to guarantee the proper operation of the system and the economic costs associated to the required communication structure. Moreover, another issue appears when there is a large amount of required data to control the system. The measuring and transmission processes, and the computation of the control inputs make closed-loop systems suffer from high computational burden.One way to overcome such problems is to consider the use of multi-agent systems framework, which may be cast in game-theoretical terms. Game theory studies interactions between self-interested agents. Particularly, this theory tackles with the problem of interaction between agents using different strategies that wish to maximize their welfares. For instance in [1], the authors provide connections between games, optimization, and learning for signal processing in networks. Other approaches in terms of learning and games can be found in [2]. In [3], distributed computation algorithms are developed based on generalized convex games that do not require full information, and where there is a dynamic change in terms of network topologies. Applications of game theory in control of optical networks and game-theoretic methods for smart grids are described in [4], [5], [6]. Another approach in game theoretical methods is to design protocols or mechanisms that possess some desirable properties [7]. This approach leads to a broad analysis of multi-agent interactions, particularly those involving negotiation and coordination problems [8]. Other game-theoretical applications to engineering are reported in [9].From a game-theoretical perspective, it can be distinguished among three types of games: matrix games, continuous games, and differential/dynamic games (see "The relationship among matricial games, full-potential population games, and resource allocation problems"). In matrix games (that generally use the normal form), individuals play simultaneously and only once, and the decision is mainly based on a static terms. In this case, players or agents are individually treated. Differently, in continuous games, players have infinitely many pure strategies [10], [11]. On the other hand, in dynamic games it is assumed that players can use some type of learning mechanism that allows them to adjust the actions taken based on their past decisions. Basically, it can be said that dynamic games are characterized by three main problems: i) how to model the environment where players interact; ii) how to model the objectives of players; and iii) how to specify the order in which the actions are taken and how much information each player possesses. In this case, it is assumed that there are interactions between large number of agents (which are usually unknown) [12].Among these dynamic game...

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“…In today´s society, where electricity consumption has experienced a significant increase in the last years, it is necessary to carry out actions which promote saving and energy efficiency for the sake of a more sustainable development [1]. One of these actions must be the introduction of electrical monitoring systems that measure, store and exploit the available information in electrical data to define responses aimed at improving energy efficient [2].…”

Section: Introductionmentioning

confidence: 99%

Advanced tools for electricity consumption monitoring

Domínguez¹,

Fuertes²,

Alonso³

et al. 2024

RE&PQJ

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Abstract. In this work, the power monitoring system developed at the University of León is presented. This system includes both traditional and advance monitoring tools based on statistical and data mining techniques. These techniques exploit data in order to search electrical patterns, detect anomalies, predict future consumption, optimize power, etc. Some results obtained from the monitoring system during year 2010 are presented.

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Intelligent Infrastructure for Energy Efficiency

Abstract: A substantial fraction of wasted energy can be recovered by extending insights from the architecture of the Internet to the infrastructure of buildings.

Cited by 60 publications

References 7 publications

Building-level occupancy data to improve ARIMA-based electricity use forecasts

Building-level occupancy data to improve ARIMA-based electricity use forecasts

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

Advanced tools for electricity consumption monitoring

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