On the long-term density prediction of peak electricity load with demand side management in buildings

Jang, Youngchan; Byon, Eunshin; Jahani, Elham; Cetin, Kristen

doi:10.1016/j.enbuild.2020.110450

Cited by 23 publications

(5 citation statements)

References 38 publications

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“…Additionally, any long-term economic projections for coal and natural gas availability and price were not considered. In this model, we utilize RCP data from downscaled climate models, which might not correctly characterize local climate accurately, inducing model bias (Jang et al 2020). Finally, this model and analysis only examine the payback period for the capital investment on the conversion project based on fuel Note: β 0 = baseline energy consumption (or intercept) of the model; and β 1 = expected change in energy consumption given a one-unit increase in HDD.…”

Section: Discussionmentioning

confidence: 99%

Infrastructure Decision-Making under Climate Change Uncertainty: Case Study of Centralized Heating at Wright-Patterson AFB, Ohio

Ferry

Delorit

Chini

2022

J. Infrastruct. Syst.

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Increased prevalence, lower cost, and reduced environmental impacts have granted natural gas status as a bridge fuel over the past decade in place of coal-fired heat and power generation across the United States. As a result, some coal facilities are retrofitting their systems to combust natural gas for heating and power. However, data and economic criteria surrounding these decisions are not widely available. Wright-Patterson Air Force Base (WPAFB) invested approximately $25 million, from 2014 to 2016, to retrofit two coal-fired steam-heatgenerating plants with natural gas equipment. Economic analyses used to inform these long-term infrastructure decisions rarely consider the uncertainty of climate change. As the uncertainty of climate change projects is large, there is a distinct need to incorporate these forecasting models in large-scale infrastructure investments to facilitate robust decision-making. In this case study, we evaluate the uncertainty surrounding infrastructure decision-making through a Monte Carlo simulation. The case study evaluates the sensitivity of cost savings and payback period for the fuel switch relative to climate predictions. In this analysis, we characterize uncertainty of the retrofit as a function of its payback period length. The payback period is expected to increase by nearly 6 years (∼20%) when factoring climate change and uncertainty into the economic analysis. This case study showcases the uncertainty surrounding large infrastructure decisions with climate change implications.

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Section: Discussionmentioning

confidence: 99%

Infrastructure Decision-Making under Climate Change Uncertainty: Case Study of Centralized Heating at Wright-Patterson AFB, Ohio

Ferry

Delorit

Chini

2022

J. Infrastruct. Syst.

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“…However, it is challenging to take account of all the factors affecting the load. Based on the extensive literature analysis [43][44][45][46], the temperature is an extremely critical factor among meteorological variables. The temperature data have been added with historical loads as input variables in this study to reduce hybrid models' computational cost.…”

Section: Short-term (30 Min)mentioning

confidence: 99%

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Huang¹,

Hasan

Deng³

et al. 2022

Energy

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“…It is expected that the electricity generation from renewables will double over the next 30 years [80]. Advanced communication capabilities, smart meter installations, mobile internet, and other smart technologies are enabling grid-responsive demand response and management services, such as shedding, shifting, and modulating load in peak and off-peak periods while minimizing occupant discomforts [224,91,127,240,168]. Additionally, increased use of battery storage technology and the growing penetration of electric vehicles will also change electricity supply and usage patterns [189,309].…”

Section: Energymentioning

confidence: 99%

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

Kontar¹,

Shi²,

Yue³

et al. 2021

Preprint

Self Cite

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The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the "cloud" will be substituted by the "crowd" where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacypreserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

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On the long-term density prediction of peak electricity load with demand side management in buildings

Cited by 23 publications

References 38 publications

Infrastructure Decision-Making under Climate Change Uncertainty: Case Study of Centralized Heating at Wright-Patterson AFB, Ohio

Infrastructure Decision-Making under Climate Change Uncertainty: Case Study of Centralized Heating at Wright-Patterson AFB, Ohio

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning

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