Scheduling residential electric loads for green house gas reductions

Stoll, Pia; Bag, Gargi; Rossebø, Judith E. Y.; Rizvanovic, Larisa; Åkerholm, Mikael

doi:10.1109/isgteurope.2011.6162805

Cited by 14 publications

(4 citation statements)

References 4 publications

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“…One example is [21], which differentiated electricity generation emission factors from consumption factors (including imported energy from neighbors), and evaluated the environmental performances of demandside management programs. Specifically for smart homes, the Swedish Active House [22] proposes an architecture in which the house communicates with the Utility company via a specialized interface to receive a 24-hour forecast of CO 2 emissions. The hourly information was not available at the time of their publication, so the authors defined a model using historical information, assuming that the production sources were dispatched according to their pricing (cheapest first), and also comprising imports and exports between adjacent regions.…”

Section: Smart Homes Energy and Emissions Managementmentioning

confidence: 99%

Time Series-Based GHG Emissions Prediction for Smart Homes

Riekstin

Langevin

Dandres

et al. 2020

IEEE Trans. Sustain. Comput.

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Smart homes play a crucial role in reducing the residential sector electricity consumption and Greenhouse Gases (GHG) emissions. In this work, we present a time series approach to predict GHG emissions to be integrated into smart home management systems. More specifically, we used Long Short-Term Memory (LSTM), a variant of Recurrent Neural Networks. The prediction results get mean absolute percentage error (MAPE) close to 2 % when the region under study has an energy matrix mostly based on fossil fuels, less intermittent. For regions in which more renewable sources are present, the MAPE is around 12 %. However, in either case, LSTM can predict the hours well with smaller emissions among the next 24 hours. Such day-ahead information brings awareness to the users and allows the scheduling of appliances to work in the hours in which the emissions are minimal, reducing them without significantly affecting the consumers' behavior.

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Section: Smart Homes Energy and Emissions Managementmentioning

confidence: 99%

Time Series-Based GHG Emissions Prediction for Smart Homes

Riekstin

Langevin

Dandres

et al. 2020

IEEE Trans. Sustain. Comput.

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“…dryers are not to be started before washing cycles are finished. Furthermore, the case study makes reference to the modern Stockholm Royal Seaport (SRS) urban district [30] by including a building representative for SRS‐specific average hourly power consumption and scheduling of appliances.…”

Section: Experimental Set‐upmentioning

confidence: 99%

Multi‐location virtual smart grid laboratory with testbed for analysis of secure communication and remote co‐simulation: concept and application to integration of Berlin, Stockholm, Helsinki

Wiezorek

Parisio

Kyntäjä

et al. 2017

IET Generation, Transmission & Distribution

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The process of advancement and validation of smart grid technologies and systems calls for the availability of diverse expertise and resources. In response to this consideration, the Virtual Smart Grid Lab (VSGL) was developed as described in this paper. At the core of the VSGL is a novel communication platform for seamlessly connecting geographically distributed laboratories with distinct competences. The platform has the dual purpose of opening access to resources of remote partner laboratory sites and offering the capability to emulate, analyze, and test smart grid communication networks involved in linking the distributed laboratory resources. The VSGL implementation is validated through a use case, in which the resources of R&D laboratories in three European countries are connected to form an aggregated system of distributed energy resources. The operation of the latter was coordinated through an energy management system based on model predictive control (MPC). The VSGL was found to be very suitable to meet the communication-specific requirements of such type of study. In addition, for this particular case the effectiveness of the MPC subject to diverse implementations of communication links was substantiated.

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“…Developed countries are trying to reduce their greenhouse gas emissions. For example, Europe has committed to reduce the emissions of greenhouse gas to at least 20% below 1990 levels and to produce not less than 20% of its energy consumption from renewable sources by 2020 [6,7]. This has opened up new dimensions for renewable energy sources and particularly for PV.…”

Section: Introductionmentioning

confidence: 97%

Comparative analysis of MPPT techniques for PV in domestic applications

Jain¹,

Vaibhav²,

Goyal³

2014

2014 6th IEEE Power India International Conference (PIICON)

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Renewable energy systems are becoming an important part of world's energy. In the view of green house gas emissions and various adverse effects of other energy sources, solar photovoltaic (PV) energy has proven its importance amongst various other renewable energy sources. But the characteristics of PV array are non linear and time varying, thus the power generated changes continuously with atmospheric conditions. Thus maximum power point tracking (MPPT) algorithms are required for extraction of effective energy from PV array. This paper focuses on an investigation of various MPPT algorithms namely Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC) and Artificial Neural Network (ANN), for PV array installed for domestic application in stand-alone mode. These algorithms are compared on the basis of the tracking time, irradiance variation and operating point operations. The paper thus presents advantages, disadvantages and characteristics of different MPPT algorithms for PV array for domestic applications.

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Scheduling residential electric loads for green house gas reductions

Cited by 14 publications

References 4 publications

Time Series-Based GHG Emissions Prediction for Smart Homes

Time Series-Based GHG Emissions Prediction for Smart Homes

Multi‐location virtual smart grid laboratory with testbed for analysis of secure communication and remote co‐simulation: concept and application to integration of Berlin, Stockholm, Helsinki

Comparative analysis of MPPT techniques for PV in domestic applications

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