mdh.sePublications
Change search
Refine search result
1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Frost, Anna. E.
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Patterns and temporal resolution in commercial and industrial typical load profiles2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 2684-2689Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 2.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks2016In: The 8th International Conference on Applied Energy – ICAE2016, 2016Conference paper (Refereed)
  • 3.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Finite State Machine household’s appliances models for non-intrusive energy estimation2016In: The 8th International Conference on Applied Energy – ICAE2016 / [ed] Energy Procedia, 2016Conference paper (Refereed)
  • 4.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Eriksson, Douglas
    Mälardalens Högskola.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    A study on the viability of an on-site combined heat- and power supply system with and without electricity storage for office building2020In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 213, article id 112807Article in journal (Refereed)
    Abstract [en]

    The building sector in the European Union accounts for over 40% of the final energy use, where the usage of non-residential buildings may be up to 40% higher than the residential sector. Improving building energy efficiency across all categories of buildings is one key goal of the European energy policies, made prominent by the Climate and Energy package, Energy Performance of Building Directive and Energy Efficiency Directive. In this study, the profitability of an on-site combined heat and power supply system for an office building is investigated. A reference model utilizing solely district heating was constructed and used for validation purposes. Then, a photovoltaic assisted ground source heat pump model was developed and investigated with and without electrical storage to reveal the most cost-effective investment scenario in cold climate regions. The reference model was validated using consumption data provided by the facility owner, after which an investigation of the energy saving potential along with the economic viability of adapting a new heat- and power supply system was conducted. It was concluded that a ground source heat pump in combination with a standalone rooftop photovoltaic system, was successful in satisfying thermal requirements while lowering the building specific energy demand compared to utilizing a district heating system. The photovoltaic assisted ground source heat pump system including a battery bank is the most profitable when incentives are granted, a higher self-consumption of 93.1% is achieved with a battery capacity of 38.4 kWh. 

  • 5.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Eskilsson, Anton
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    An open-source visualization platform for energy flows mapping and enhanced decision making2019In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 3208-3214Conference paper (Refereed)
    Abstract [en]

    Visualization of energy consumption within the built environment, both in the private and public sectors, can be a potent tool for increasing conservation behavior. For instance, dynamics visualization could add new knowledge to the end-users to have a better understanding of the energy flows, dynamic mapping of the energy usage in order to avoid misplacing effort and resources, e.g. when it comes to selection of heating systems, investing in energy efficiency measures and renewables as well as when stakeholders are planning for new area to be populated with either commercial or residential buildings. This paper introduces an open-source visualization platform allowing various energy flows mapping in both time and space of a sports facilities. It further includes advanced functionalities such as key performance indicators and integrated prediction models to assist the benchmarking and decision making processes.

  • 6.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Eskilsson, Anton
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Energy flow mapping and key performance indicators for energy efficiency support: A case study a sports facility2019In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 4350-4356Conference paper (Refereed)
    Abstract [en]

    This paper aims to investigate the energy consumption in a sport facilities and elaborate a set of novel energy indicators to support decision making process. Sports facilities are complex systems having higher significant energy demand than other facilities for service and recreation. These facilities require massive demand of various energy (e.g. heat, cooling, electricity) to meet the requirement of different types of sports facilities leading to a high complexity to understand and describe such facility accurately. To tackle this problem, an energy flow mapping of different energy demand is developed to have more insights on the energy flow in both time and space domain within one of the biggest sports facilities in Sweden, Rocklunda arena. All the energy meters are virtually connected to design a comprehensive mapping of the energy streams. Then the data is processed and analyzed to elaborate a set of novel key performance indicators KPIs allowing a simplistic description of the different aspects of the system consumption profile and the related energy performance.

  • 7.
    Maher, Azaza
    et al.
    Universite de Tunis-El-Manar, Tunisia.
    Kamel, E.
    Universite de Tunis-El-Manar, Tunisia.
    Enrico, F.
    University of Turin, Italy.
    Atif, I.
    University of Qatar, Qatar.
    An intelligent system for the climate control and energy savings in agricultural greenhouses2016In: Energy Efficiency, ISSN 1570-646X, E-ISSN 1570-6478, Vol. 9, no 6, p. 1241-1255Article in journal (Other academic)
    Abstract [en]

    A greenhouse for crop production is a complex thermodynamic system where the indoor temperature and the humidity conditions have a great impact on the crop yields. This system can be considered a multivariable input output system MIMO. This paper aims at presenting a physical model of a greenhouse, experimentally validated, in order to propose a fuzzy-based controller to manage the indoor climate of a greenhouse using some actuators (induction motors, heating system etc.aEuro broken vertical bar) for ventilation, heating, humidifying, and dehumidifying purposes. In addition, a novel approach is presented for energy management by involving the photovoltaic energy in order to minimize the use of conventional electrical grid and to lower costs of agriculture production. The photovoltaic (PV) generator will serve to power a direct torque control (DTC) controlled induction motor which drive a variable speed fan. The validation of the physical model shows a high agreement with the experimental measurement. The simulation results show the effectiveness of the fuzzy controller as well as the PV generator for saving the energy and lowering the costs of crop production into greenhouses.

  • 8.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Evaluation of classification methodologies and Features selection from smart meter data2017In: Energy Procedia, Elsevier Ltd , 2017, p. 2250-2256Conference paper (Refereed)
    Abstract [en]

    The choice of the classification algorithm to map the feature vector to a known labelled database signature is an important step toward loads identification in non-intrusive load monitoring NILM. In this paper, we investigate the quality of load recognition when using various smart features and the commonly used classification algorithms. A low error rate is observed when using classification tree DT, k-NN and support vector machine SVM classifier, the error rate ranges between 20 % and 29 %. Among the smart meter features, the current waveform, the active/reactive power and the transient features have higher interesting recognition results when associated with a specific classifier.

  • 9.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Finite State Machine Household's Appliances Models for Non-intrusive Energy Estimation2017In: Energy Procedia, Elsevier Ltd , 2017, p. 2157-2162Conference paper (Refereed)
    Abstract [en]

    Non-intrusive loads monitoring NILM is a set of algorithms that aims to leverage smart meter data by extracting more useful information from the smart meter data. NILM involves disaggregation of individual household loads in term of their individual energy consumption. It is considered as low cost alternative to better understand the electrical network and reduce complexity of the management operations. It offers to households monitoring and control possibilities to their everyday energy consumption. This paper contributes toward non-intrusive energy estimation of household's loads through data-driven appliances modelling approach based on finite state machine models that mimic the real operations cycle. First, the models are built based on features extractions and events clustering via dynamic fuzzy clustering. The resulting clusters are further de-noised and processed to reveal accurate appliances operations states. Then finite state machine models are created using transition probability matrix and an optimization approach to extract the operation cycle that best describe real appliance operations. The evaluation of the framework was performed using two public datasets showing its performance to learn appliances models and energy estimation with an average error of 5% to 22%. © 2017 The Authors.

  • 10.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden2017In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 123, p. 108-118Article in journal (Refereed)
    Abstract [en]

    Distributed energy resources DERs are small scale energy system which could provide local supply when placed at customers' premises. They aggregate multiple local and diffuse production installations, consumer facilities, storage facilities and monitoring tools and demand management. The main challenges when assessing the performance of an off-grid hybrid micro-grid system HMGS are the reliability of the system, the cost of electricity production and the operation environmental impact. Hence the tradeoff between three conflicting objectives makes the design of an optimal HMGS seen as a multi-objective optimization task. In this paper, we consider the optimization and the assessment of a HMGS in different Swedish cities to point out the potential of each location for HMGS investment. The HMGS consists of photovoltaic panels, wind turbines, diesel generator and battery storage. The HMGS model was simulated under one-year weather conditions data. A multi objective particle swarm optimization is used to find the optimal system configuration and the optimal component size for each location. An energy management system is applied to manage the operation of the different component of the system when feeding the load. The techno economics analysis shows the potential of HMGS in the Swedish rural development. (C) 2017 Elsevier Ltd. All rights reserved.

  • 11.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Smart meter data clustering using consumption indicators: responsibility factor and consumption variability2017In: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY / [ed] Yan, J Wu, J Li, H, ELSEVIER SCIENCE BV , 2017, p. 2236-2242Conference paper (Refereed)
    Abstract [en]

    The wide spread of smart metering roll out enables a better understanding of the consumer behavior and tailoring demand response DR programs to achieve cost-efficient energy savings. In the residential sector smart metering allows detailed readings of the power consumption in the form of large volumes time series that encodes relevant information for distribution network operators DNOs to manage in optimal ways low-voltage networks. Further, DNOs may leverage the smart meter data to identify customer group for energy efficiency programs and demand side response DSR (e.g., dynamic pricing schemes). In this paper, we outline the application of smart meter data mining to identify consumers who are more responsible for the peak system using responsibility factor and consumption variability. Identification of consumers having higher responsibility to the peak system may yield to better enhance energy reduction recommendations and enable more tailored dynamic pricing plans depending on the consumer's influence on the utility peak. Responsibility factor and consumption variance have been investigated as input features of the clustering algorithms. Two clustering techniques, hierarchical clustering and self-organising map SOM, have been used to study the resulting customer groups and to have an effective graphical visualization of the customer's cluster distribution on the input feature space.

  • 12.
    Maher, Azaza
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Supervised Household’s Loads Pattern Recognition2016In: 2016 IEEE Electrical Power and Energy Conference, EPEC 2016 / [ed] IEEE, 2016, article id 7771718Conference paper (Refereed)
    Abstract [en]

    The deployment of smart meters is a promising innovation that comes to enhance the energy efficiency measures in the smart grid. The smart meter enables distributors to better understand the electrical network and reduce complexity of the management operations. It offers to households monitoring and control possibilities to their everyday energy consumption through the distribution of detailed information on household consumption and its evolution. This involves disaggregation of individual household loads in term of their individual energy consumption known as Non intrusive loads monitoring. In this paper, we present a supervised NILM approach based on dynamic fuzzy c-means events clustering and KNN label matching. First, a filtering method is involved to enhance the edge/events detection step. Then we perform a dynamic Fuzzy c-means clustering procedures to build appliances signature data based on active and reactive power measurements taking into account the time of day usage. The data base is further refined to map potential clusters centers that best identify the different appliances. A performance evaluation of the proposed approach is conducted showing a recognition rate over 90% for high consumption loads and promising results for low consumption loads.

  • 13.
    Sandberg, Alexander
    et al.
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Wallin, Fredrik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Li, Hailong
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks2017In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, p. 3784-3790Article in journal (Refereed)
    Abstract [en]

    With the building sector standing for a major part of the world's energy usage it of utmost importance to develop new ways of reduce the consumption in the sector. This paper discusses the evolution of the regulations and policies of the Swedish electric and district heating metering markets followed by the development of a nonlinear autoregressive neural network with external input (NARX), with the purpose of performing heat demand forecasts for a commercial building in Sweden. The model contains 13 input parameters including; calendar, weather, energy and social behavior parameters. The result revealed that these input parameters can predict the building heat demand to 96% accuracy on an hourly basis for the period of a whole year. Further analysis of the result indicates that the current data resolution of the district heat measuring system limits the future possibilities for services compared to the electric metering system. This is something to consider when new regulation and policies is formulated in the future.

    Download full text (pdf)
    fulltext
  • 14.
    Trosten, Torbjörn
    et al.
    Mälardalen University, School of Innovation, Design and Engineering. Bombardier Transportation, Västerås, Sweden.
    Moskull, Henrik
    Bombardier Transportation, Västerås, Sweden.
    Lindahl, Martin
    Bombardier Transportation, Västerås, Sweden.
    Dahlquist, Erik
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Maher, Azaza
    Mälardalen University, School of Business, Society and Engineering, Future Energy Center.
    Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter2018In: Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter, 2018Conference paper (Refereed)
    Abstract [en]

    The introduction of Silicon Carbide (SiC) MOSFET based inverters into the traction drive makes it possible to increase the inverter switching frequency and reduce energy consumption. This paper describes how to model switching frequency dependent losses in the traction drive and compares the calculated losses to measurements done on a newly developed SiC MOSFET based traction drive. The results from the developed loss models of motor and inverter agrees well with the results from energy measurements. This paper concludes that the energy use of the traction motor and inverter can be simulated well using simple models where skin-effect losses in the motor are modelled in detailed. This paper also concludes that in terms of energy efficiency, the optimal switching frequency using a SiC MOSFET based inverter, is in the range of 3-6 kHz.

1 - 14 of 14
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf