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  • 1.
    Idowu, Samuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Saguna, Saguna
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Schelén, Olov
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Applied Machine Learning: Forecasting Heat Load in District Heating System2016In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 133, 478-488 p.Article in journal (Refereed)
    Abstract [en]

    Forecasting energy consumption in buildings is a key step towards the realization of optimized energy production, distribution and consumption. This paper presents a data driven approach for analysis and forecast of aggregate space and water thermal load in buildings. The analysis and the forecast models are built using district heating data unobtrusively collected from ten residential and commercial buildings located in Skellefteå, Sweden. The load forecast models are generated using supervised machine learning techniques, namely, support vector machine, regression tree, feed forward neural network, and multiple linear regression. The model takes the outdoor temperature, historical values of heat load, time factor variables and physical parameters of district heating substations as its input. A performance comparison among the machine learning methods and identification of the importance of models input variables is carried out. The models are evaluated with varying forecast horizons of every hour from 1 up to 48 hours. Our results show that support vector machine, feed forward neural network and multiple linear regression are more suitable machine learning methods with lower performance errors than the regression tree. Support vector machine has the least normalized root mean square error of 0.07 for a forecast horizon of 24 hour.

  • 2.
    Schmidt, Mischa
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. NEC Laboratories Europe, Heidelberg.
    Moreno, M. Victoria
    Research Institute of Energy and Environment of Heidelberg (ifeu), Germany.
    Schülke, Anett
    NEC Laboratories Europe, Heidelberg, Germany.
    Macek, Karel
    Honeywell ACS Global Labs, Prague, Czech Republic.
    Mařik, Karel
    Honeywell ACS Global Labs, Prague, Czech Republic.
    Pastor, Alfonso Gordaliza
    Department of Technical Studies, Veolia Servicios LECAM, Valladolid, Spain.
    Optimizing legacy building operation: the evolution into data-driven predictive cyber-physical systems2017In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 148, 257-279 p.Article in journal (Refereed)
    Abstract [en]

    Fossil fuels serve a substantial fraction of global energy demand, and one major energy consumer is the global building stock. In this work, we propose a framework to guide practitioners intending to develop advanced predictive building control strategies. The framework provides the means to enhance legacy and modernized buildings regarding energy efficiency by integrating their available instrumentation into a data-driven predictive cyber-physical system. For this, the framework fuses two highly relevant approaches and embeds these into the building context: the generic model-based design methodology for cyber-physical systems and the cross-industry standard process for data mining. A Spanish school's heating system serves to validate the approach. Two different data-driven approaches to prediction and optimization are used to demonstrate the methodological flexibility: (i) a combination of Bayesian regularized neural networks with genetic algorithm based optimization, and (ii) a reinforcement learning based control logic using fitted Q-iteration are both successfully applied. Experiments lasting a total of 43 school days in winter 2015/2016 achieved positive effects on weather-normalized energy consumption and thermal comfort in day-to-day operation. A first experiment targeting comfort levels comparable to the reference period lowered consumption by one-third. Two additional experiments raised average indoor temperatures by 2 K. The better of these two experiments only consumed 5% more energy than the reference period. The prolonged experimentation period demonstrates the cyber-physical system-based approach's suitability for improving building stock energy efficiency by developing and deploying predictive control strategies within routine operation of typical legacy buildings.

  • 3.
    Shadram, Farshid
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Johansson, Tim
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Lu, Weizhuo
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Schade, Jutta
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Olofsson, Thomas
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    An integrated BIM-based framework for minimizing embodied energy during building design2016In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 128, 592-604 p.Article in journal (Refereed)
    Abstract [en]

    Assessment of the embodied energy associated with the production and transportation of materials during the design phase of building provides great potential to profoundly affect the building’s energy use and sustainability performance. While Building Information Modeling (BIM) gives opportunities to incorporate sustainability performance indicators in the building design process, it lacks interoperability with the conventional Life Cycle Assessment (LCA) tools used to analyse the environmental footprints of materials in building design. Additionally, many LCA tools use databases based on industry-average values and thus cannot account for differences in the embodied impacts of specific materials from individual suppliers. To address these issues, this paper presents a framework that supports design decisions and enables assessment of the embodied energy associated with building materials supply chain based on suppliers’ Environmental Product Declarations (EPDs). The framework also integrates Extract Transform Load (ETL) technology into the BIM to ensure BIM-LCA interoperability, enabling an automated or semi-automated assessment process. The applicability of the framework is tested by developing a prototype and using it in a case study, which shows that a building’s energy use and carbon footprint can be significantly reduced during the design phase by accounting the impact of individual material in the supply chain.

  • 4.
    Shadram, Farshid
    et al.
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    Mukkavaara, Jani
    Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Industrilized and sustainable construction.
    An Integrated BIM-based framework for the optimization of the trade-off between embodied and operational energy2018In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 158, 1189-1205 p.Article in journal (Refereed)
    Abstract [en]

    Design choices with a unilateral focus on the reduction of operational energy for developing energy-efficient and near-zero energy building practices can increase the impact of the embodied energy, as there is a trade-off between embodied and operational energy. Multi-objective optimization approaches enable exploration of the trade-off problems to find sustainable design strategies, but there has been limited research in applying it to find optimal design solution(s) considering the embodied versus operational energy trade-off. Additionally, integration of this approach into a Building Information Modeling (BIM) for facilitating set up of the building model toward optimization and utilizing the benefits of BIM for sharing information in an interoperable and reusable manner, has been mostly overlooked. To address these issues, this paper presents a framework that supports the making of appropriate design decisions by solving the trade-off problem between embodied and operational energy through the integration of a multi-objective optimization approach with a BIM-driven design process. The applicability of the framework was tested by developing a prototype and using it in a case study of a low energy dwelling in Sweden, which showed the potential for reducing the building’s Life Cycle Energy (LCE) use by accounting for the embodied versus operational energy trade-off to find optimal design solution(s). In general, the results of the case study demonstrated that in a low energy dwelling, depending on the site location, small reductions in operational energy (i.e. 140 GJ) could result in larger increases in embodied energy (i.e. 340 GJ) and the optimization process could yield up to 108 GJ of LCE savings relative to the initial design. This energy saving was equivalent to up to 8 years of the initial design’s operational energy use for the dwelling, excluding household electricity use.

  • 5. Yliniemi, Kimmo
    et al.
    Delsing, Jerker
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    van Deventer, Jan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.
    Experimental verification of a method for estimating energy for domestic hot water production in a 2-stage district heating substation2009In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 41, no 2, 169-174 p.Article in journal (Refereed)
    Abstract [en]

    In this paper we compare our estimate of energy consumption for domestic hot water production in a building with the measured value. The energy consumption for hot water production is estimated from the measured total power consumption. The estimation method was developed using computer simulations, and it is based on the assumption that hot water production causes rapid and detectable changes in power consumption. A comparison of our estimates with measurements indicates that the uncertainty in estimation of hot water energy consumption is ±10%. Thus, the estimate is comparable to class 3 energy meter measurements, which have an uncertainty of ±2-10%.

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