City-level energy and CO2 reduction effect by introducing new residential water heaters

Shimoda, Yoshiyuki; Okamura, Tomo; Yamaguchi, Yohei; Yamaguchi, Yukio; Taniguchi, Ayako; Morikawa, Takao

doi:10.1016/j.energy.2010.08.043

Cited by 39 publications

(21 citation statements)

References 9 publications

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“…Regarding the application of the load models reported in the literature, they are usually employed with the aim of foreseeing the impact caused by the deployment of different DSM strategies. focus on evaluating the effects of energy conservation measures based on improving heat insulation of buildings or employing energy‐efficient appliances. assesses the impact of DSM actions in the cold load pickup and diversity factor.…”

Section: Introductionmentioning

confidence: 99%

“…Although all of them consist of the reproduction of the physical phenomena that occur in the loads, they differ on the type of loads considered and on the individual load models formulated, as well as on the aggregation process. Most of them are focused on the simulation of thermostatically controlled loads, mainly air-conditioning and electric space-heating systems [3][4][5][6][7][8][9][10][11][12][13] or electric water heating systems [6,8,14,17]. Few studies take into account other sorts of loads that are not thermostatically controlled such as washing machines or dish-washers [5,15].Different approaches can be found regarding the aggregation procedure.…”

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confidence: 99%

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Residential load forecasting under a demand response program based on economic incentives

Ruiz

Claessens

Jimeno

et al. 2014

Int. Trans. Electr. Energ. Syst.

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Summary This paper describes a tool for an Aggregator to forecast the aggregated load demand response of a group of domestic customers subscribed to an indirect load control program based on price/volume signals. The tool employs a bottom‐up approach based on physical end‐use load models where the individual responses of a random sample of customers are combined in order to build the aggregated load demand response model. Simulation of the individual responses is carried out with an optimization algorithm based on mixed integer linear programming that minimizes the electricity bill whilst maintaining consumer's comfort level. To improve the performance of the model, a genetic algorithm for fitting the input parameters according to measured data is also provided. The tool is intended to allow the Aggregator rehearsing the impact of different control strategies and therefore choosing the most appropriate ones for market participation and portfolio optimization. To exemplify the methodological applicability of the developed algorithm, a case study based on an actual power system in eastern Spain is considered. Copyright © 2014 John Wiley & Sons, Ltd.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Residential load forecasting under a demand response program based on economic incentives

Ruiz

Claessens

Jimeno

et al. 2014

Int. Trans. Electr. Energ. Syst.

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“…Simulation and optimization are also used, primarily to assess the potential impacts of a policy change. Notable examples include the effect of urban form on energy use for water systems [225], predicting greenhouse-gas emissions for a Japanese city [226,227], and the impact of transportation system changes [228][229][230][231][232].…”

Section: Case Studies Of What Work and What Doesn't In Urban Energymentioning

confidence: 99%

A review of urban energy system models: Approaches, challenges and opportunities

Keirstead

Jennings

Sivakumar

2012

Renewable and Sustainable Energy Reviews

524

238

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Energy use in cities has attracted significant research in recent years. However such a broad topic inevitably results in number of alternative interpretations of the problem domain and the modelling tools used in its study. This paper seeks to pull together these strands by proposing a theoretical definition of an urban energy system model and then evaluating the state of current practice. Drawing on a review of 219 papers, five key areas of practice were identified -technology design, building design, urban climate, systems design, and policy assessment -each with distinct and incomplete interpretations of the problem domain. We also highlight a sixth field, land use and transportation modelling, which has direct relevance to the use of energy in cities but has been somewhat overlooked by the literature to date.Despite their diversity, these approaches to urban energy system modelling share four common challenges in understanding model complexity, data quality and uncertainty, model integration, and policy relevance. We then examine the opportunities for improving current practice in urban energy systems * Corresponding author Email addresses: j.keirstead@imperial.ac.uk (James Keirstead), m.jennings09@imperial.ac.uk (Mark Jennings), a.sivakumar@imperial.ac.uk (Aruna Sivakumar) Preprint submitted to Renewable and Sustainable Energy ReviewsOctober 5, 2011modelling, focusing on the potential of sensitivity analysis and cloud computing, data collection and integration techniques, and the use of activity-based modelling as an integrating framework. The results indicate that there is significant potential for urban energy systems modelling to move beyond single disciplinary approaches towards a sophisticated integrated perspective that more fully captures the theoretical intricacy of urban energy systems.

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“…Total energy consumption for the residential stock was estimated by multiplying the simulated energy use and the number of households and then summing them up. This concept of stock modeling has been developed and employed in a series of research (Shimoda et al 2004(Shimoda et al , 2010aYamaguchi et al 2008). These residential building stock models have core simulation engines tailored for residential buildings and specific regions.…”

Section: Existing Building Stock Energy Prediction Modelsmentioning

confidence: 99%

Review on stochastic modeling methods for building stock energy prediction

Lim

Zhai

2017

Build. Simul.

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Increasing risks of energy security and greenhouse gas emission due to the growing urbanization trend have prompted the need for urban energy demand prediction and management, in which the building energy consumption is the main cause. This paper reviews the recent advances and state-of-the-art in modeling building stock energy consumption, including both the top-down and bottom-up approaches. The study compares and summarizes the strengths and weaknesses of each primary method. Specific focus has been paid to the bottom-up stochastic engineering modeling methods, which hold sound quantitative theory bases as well as considering uncertain reality conditions. Stochastic building stock energy models account for the uncertainties that are the main limitation in existing building stock models. Discussions are provided regarding the process in the current stochastic building stock energy model. Challenges and possible future directions are examined for the improvement of stochastic building stock energy model.

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City-level energy and CO2 reduction effect by introducing new residential water heaters

Cited by 39 publications

References 9 publications

Residential load forecasting under a demand response program based on economic incentives

Residential load forecasting under a demand response program based on economic incentives

A review of urban energy system models: Approaches, challenges and opportunities

Review on stochastic modeling methods for building stock energy prediction

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