Malmö University Publications
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  • 1.
    Dabaieh, Marwa
    et al.
    Malmö University, Faculty of Culture and Society (KS), Department of Urban Studies (US).
    Iarkov, Ilia
    Lund University.
    Rodil, Kasper
    Aalborg University.
    The ‘Z-Free’ Home: A Circular Thinking and Eco-Cycle Design Practice2023In: Energies, E-ISSN 1996-1073, Vol. 16, p. 1-20, article id 6536Article in journal (Refereed)
    Abstract [en]

    In recent years, the need for affordable sustainable housing has increased. At the same time, there has been a gradual rising interest in compact living. With the mounting impacts of climate change, a new way of thinking is needed to develop more resilient and climate responsive ways of living that are compact, affordable, and climate-conscious. In response to this need, the idea of a ‘Z-Free Home’ was born. The ‘Z-Free Home’ is a tiny mobile house equipped with essential passive and eco-cycle systems that achieves nine zero targets. The main design and construction concept is based on circular design and a return to nature life cycle principles. In this paper, the architectural design concept, building energy modelling, and simulation for the Z-Free Home design proposal is discussed. This paper describes the concept design and design development phases together with building modelling and simulation. A focus was made on the use of virtual reality in design development assessment as a new method for evaluating passive and eco-cycle systems. The results show that it’s possible to achieve nine different zero goals while the analysis illustrates the challenges in achieving them. The paper also described the next steps planned for the proof of concept, i.e., the 1:1 house model. The project is ongoing, and it aims at a full-scale physical prototype as a proof of concept for the zero targets. The ‘Z-Free Home’ is designed for the cold Swedish climate but could be more widely applicable in other mild climates as well as hot climates.

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  • 2. Du, Sunwen
    et al.
    Feng, Guorui
    Wang, Jianmin
    Feng, Shizhe
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). Malmö University, Internet of Things and People (IOTAP).
    Li, Zhixiong
    A New Machine-Learning Prediction Model for Slope Deformation of an Open-Pit Mine: An Evaluation of Field Data2019In: Energies, E-ISSN 1996-1073, Vol. 12, no 7, article id 1288Article in journal (Refereed)
    Abstract [en]

    Effective monitoring of the slope deformation of an open-pit mine is essential for preventing catastrophic collapses. It is a challenging task to accurately predict slope deformation. To this end, this article proposed a new machine-learning method for slope deformation prediction. Ground-based interferometric radar (GB-SAR) was employed to collect the slope deformation data from an open-pit mine. Then, an ensemble learner, which aggregated a set of weaker learners, was proposed to mine the GB-SAR field data, delivering a slope deformation prediction model. The evaluation of the field data acquired from the Anjialing open-pit mine demonstrates that the proposed slope deformation model was able to precisely predict the slope deformation of the monitored mine. The prediction accuracy of the super learner was superior to those of all the independent weaker learners.

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  • 3.
    El Gohary, Fouad
    et al.
    Uppsala Univ, Dept Civil & Ind Engn, Box 169, S-75104 Uppsala, Sweden..
    Nystrom, Sofie
    KTH Royal Inst Technol, Dept Sustainable Dev Environm Sci & Engn SEED, Tekn Ringen 10 B, S-10044 Stockholm, Sweden..
    Reitsma, Lizette
    Malmö University, Faculty of Culture and Society (KS), School of Arts and Communication (K3).
    Bartusch, Cajsa
    Uppsala Univ, Dept Civil & Ind Engn, Box 169, S-75104 Uppsala, Sweden..
    Identifying Challenges in Engaging Users to Increase Self-Consumption of Electricity in Microgrids2021In: Energies, E-ISSN 1996-1073, Vol. 14, no 5, article id 1257Article in journal (Refereed)
    Abstract [en]

    A microgrid's self-consumption rate reflects its ability to retain its own energy and decrease its reliance on the synchronous grid. This paper investigates the empirical case of a microgrid equipped with photovoltaic (PV) panels and identifies challenges in engaging the microgrid's users to increase their self-consumption. Accordingly, we explored both the physical and social dimensions of the microgrid. The former involved mapping the electricity consumption and production through an exploratory data analysis, and evaluating the associated price signals, while the latter involved the use of design interventions to explore users' perceptions of the system. We highlight the problem of price signal impedance, the need for cost reflective pricing and the challenge in designing and extending internal price models in settings with various actors. We address the limitations of price signals, alongside alternative unidimensional signals, and emphasize the need for an integrated approach to a user engagement strategy as well as the challenges that this approach entails. Our results shed light on the complexity of energy communities such as microgrids, and why their implementation can introduce multidimensional challenges that demand cross-disciplinary approaches.

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  • 4. Guo, Tao
    et al.
    He, Wei
    Jiang, Zhonglian
    Chu, Xiumin
    Malekian, Reza
    Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT).
    Li, Zhixiong
    An Improved LSSVM Model for Intelligent Prediction of the Daily Water Level2019In: Energies, E-ISSN 1996-1073, Vol. 12, no 1, article id 112Article in journal (Refereed)
    Abstract [en]

    Daily water level forecasting is of significant importance for the comprehensive utilization of water resources. An improved least squares support vector machine (LSSVM) model was introduced by including an extra bias error control term in the objective function. The tuning parameters were determined by the cross-validation scheme. Both conventional and improved LSSVM models were applied in the short term forecasting of the water level in the middle reaches of the Yangtze River, China. Evaluations were made with both models through metrics such as RMSE (Root Mean Squared Error), MAPE (Mean Absolute Percent Error) and index of agreement (d). More accurate forecasts were obtained although the improvement is regarded as moderate. Results indicate the capability and flexibility of LSSVM-type models in resolving time sequence problems. The improved LSSVM model is expected to provide useful water level information for the managements of hydroelectric resources in Rivers.

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