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Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits

Energies (Basel), 2020-05, Vol.13 (10), p.2574

2020

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Controlling a retailer's short-term financial risk exposure using demand response

IET generation, transmission & distribution, 2019-11, Vol.13 (22), p.5160-5170

2019

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Predicting the presence of hazardous materials in buildings using machine learning

Building and environment, 2022-04, Vol.213, p.108894, Article 108894

2022

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Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks

Expert systems with applications, 2023-07, Vol.222, p.119812, Article 119812

2023

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Indoor radon interval prediction in the Swedish building stock using machine learning

Building and environment, 2023-11, Vol.245, p.110879, Article 110879

2023

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Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data

Energy and buildings, 2015-12, Vol.108, p.279-290

2015

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Forecasting household consumer electricity load profiles with a combined physical and behavioral approach

Applied energy, 2014-10, Vol.131, p.267-278

2014

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Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings

Resources, conservation and recycling, 2023-12, Vol.199, p.107253, Article 107253

2023

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Evaluating the indoor radon concentrations in the Swedish building stock using statistical and machine learning

Journal of physics. Conference series, 2023-12, Vol.2654 (1), p.12086

2023

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Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation

Applied energy, 2016, Vol.162, p.472-485

2016

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A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories

Sustainability, 2021, Vol.13 (14), p.7836

2021

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Tracing Hazardous Materials in Registered Records: A Case Study of Demolished and Renovated Buildings in Gothenburg

Journal of physics. Conference series, 2021, Vol.2069 (1), p.12234

2021

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Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings

IOP conference series. Earth and environmental science, 2022-12, Vol.1122 (1), p.12050

2022

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Simulating occupancy in office buildings with non-homogeneous Markov chains for Demand Response analysis

2015 IEEE Power & Energy Society General Meeting, 2015, p.1-5

2015

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Vehicle to grid: system reference architectures and Monte Carlo simulations

International journal of vehicle autonomous systems, 2013, Vol.11 (2/3), p.205

2013

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Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks

International journal of emerging electric power systems, 2013-10, Vol.14 (5), p.421-431

2013

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A Local Market Model for Urban Residential Microgrids with Distributed Energy Resources

2012 45th Hawaii International Conference on System Sciences, 2012, p.1949-1958

2012

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Domestic heat load aggregation strategies for wind following in electric distribution systems

2013 IEEE Power & Energy Society General Meeting, 2013, p.1-5

2013

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Machine Learning in Hazardous Building Material Management: Research Status and Applications

Recent Progress in Materials, 2021-05, Vol.3 (2), p.1-1

2021

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Clustering Residential Customers with Smart Meter data using a Data Analytic Approach - External Validation and Robustness Analysis

2019 9th International Conference on Power and Energy Systems (ICPES), 2019, p.1-6

2019

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Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling

2017 IEEE International Conference on Smart Grid Communications (SmartGridComm), 2017, p.338-343

2017

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Assessment of congestion management potential in distribution networks using demand-response and battery energy storage

2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2015, p.1-10

2015

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Stochastic evaluation of aggregator business models - Optimizing wind power integration in distribution networks

2014 Power Systems Computation Conference, 2014, p.1-8

2014

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Vehicle to Grid - Monte Carlo simulations for optimal Aggregator strategies

2010 International Conference on Power System Technology, 2010, p.1-8

2010

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A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden

2013 10th International Conference on the European Energy Market (EEM), 2013, p.1-4

2013

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Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits

Controlling a retailer's short-term financial risk exposure using demand response

Predicting the presence of hazardous materials in buildings using machine learning

Estimating the probability distributions of radioactive concrete in the building stock using Bayesian networks

Indoor radon interval prediction in the Swedish building stock using machine learning

Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data

Forecasting household consumer electricity load profiles with a combined physical and behavioral approach

Machine learning models for the prediction of polychlorinated biphenyls and asbestos materials in buildings

Evaluating the indoor radon concentrations in the Swedish building stock using statistical and machine learning

Modeling office building consumer load with a combined physical and behavioral approach: Simulation and validation

A Data-Driven Approach to Assess the Risk of Encountering Hazardous Materials in the Building Stock Based on Environmental Inventories

Tracing Hazardous Materials in Registered Records: A Case Study of Demolished and Renovated Buildings in Gothenburg

Modeling Artificial Neural Networks to Predict Asbestos-containing Materials in Residential Buildings

Simulating occupancy in office buildings with non-homogeneous Markov chains for Demand Response analysis

Vehicle to grid: system reference architectures and Monte Carlo simulations

Modelling Framework and the Quantitative Analysis of Distributed Energy Resources in Future Distribution Networks

A Local Market Model for Urban Residential Microgrids with Distributed Energy Resources

Domestic heat load aggregation strategies for wind following in electric distribution systems

Machine Learning in Hazardous Building Material Management: Research Status and Applications

Clustering Residential Customers with Smart Meter data using a Data Analytic Approach - External Validation and Robustness Analysis

Aggregator strategy for planning demand response resources under uncertainty based on load flexibility modeling

Assessment of congestion management potential in distribution networks using demand-response and battery energy storage

Stochastic evaluation of aggregator business models - Optimizing wind power integration in distribution networks

Vehicle to Grid - Monte Carlo simulations for optimal Aggregator strategies

A seasonal ARIMA model with exogenous variables for elspot electricity prices in Sweden

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