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Maher, Azaza
Publications (10 of 13) Show all publications
Maher, A., Eskilsson, A. & Wallin, F. (2019). An open-source visualization platform for energy flows mapping and enhanced decision making. In: Energy Procedia: . Paper presented at 10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018 (pp. 3208-3214). Elsevier Ltd, 158
Open this publication in new window or tab >>An open-source visualization platform for energy flows mapping and enhanced decision making
2019 (English)In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 3208-3214Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Decision making, Energy mapping, Smart metering, Visualization, Benchmarking, Energy efficiency, Energy utilization, Flow visualization, Mapping, Built environment, Decision making process, Efficiency measure, Integrated prediction models, Key performance indicators, Residential building, Visualization platforms
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-43185 (URN)10.1016/j.egypro.2019.01.1006 (DOI)000471031703089 ()2-s2.0-85063900381 (Scopus ID)
Conference
10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-07-11Bibliographically approved
Maher, A., Eskilsson, A. & Wallin, F. (2019). Energy flow mapping and key performance indicators for energy efficiency support: A case study a sports facility. In: Energy Procedia: . Paper presented at 10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018 (pp. 4350-4356). Elsevier Ltd, 158
Open this publication in new window or tab >>Energy flow mapping and key performance indicators for energy efficiency support: A case study a sports facility
2019 (English)In: Energy Procedia, Elsevier Ltd , 2019, Vol. 158, p. 4350-4356Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Energy flow mapping, Key Performance Indicators, Smart metering, Sports facility, Benchmarking, Decision making, Electric measuring instruments, Energy management, Energy utilization, Mapping, Recreation centers, Sports, Decision making process, Efficiency supports, Energy flow, Energy indicator, Energy performance, Sport facilities, Energy efficiency
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-43186 (URN)10.1016/j.egypro.2019.01.785 (DOI)000471031704109 ()2-s2.0-85063891899 (Scopus ID)
Conference
10th International Conference on Applied Energy, ICAE 2018, 22 August 2018 through 25 August 2018
Available from: 2019-04-25 Created: 2019-04-25 Last updated: 2019-07-11Bibliographically approved
Trosten, T., Moskull, H., Lindahl, M., Dahlquist, E. & Maher, A. (2018). Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter. In: Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter: . Paper presented at 10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China.
Open this publication in new window or tab >>Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter
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2018 (English)In: Energy Optimal Switching Frequency for a 750V Metro Traction Drive Using Silicon Carbide MOSFET Inverter, 2018Conference paper, Poster (with or without abstract) (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.

National Category
Control Engineering
Identifiers
urn:nbn:se:mdh:diva-42582 (URN)
Conference
10th International Conference on Applied Energy (ICAE2018), 22-25 August 2018, Hong Kong, China
Available from: 2019-02-06 Created: 2019-02-06 Last updated: 2019-06-03Bibliographically approved
Sandberg, A., Wallin, F., Li, H. & Maher, A. (2017). An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks. Energy Procedia, 3784-3790
Open this publication in new window or tab >>An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks
2017 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, p. 3784-3790Article in journal (Refereed) Published
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.

National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-37548 (URN)10.1016/j.egypro.2017.03.884 (DOI)000404967903130 ()2-s2.0-85020704281 (Scopus ID)
Available from: 2017-12-22 Created: 2017-12-22 Last updated: 2018-07-25Bibliographically approved
Maher, A. & Wallin, F. (2017). Evaluation of classification methodologies and Features selection from smart meter data. In: Energy Procedia: . Paper presented at 9th International Conference on Applied Energy, ICAE 2017, 21 August 2017 through 24 August 2017 (pp. 2250-2256). Elsevier Ltd
Open this publication in new window or tab >>Evaluation of classification methodologies and Features selection from smart meter data
2017 (English)In: Energy Procedia, Elsevier Ltd , 2017, p. 2250-2256Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2017
Keywords
Feature selection, Loads recognition, NILM, Smart meter, Feature extraction, Image retrieval, Nearest neighbor search, Smart meters, Support vector machines, Classification algorithm, Classification methodologies, Classification trees, Features selection, Loads identification, Nonintrusive load monitoring, Transient features, Classification (of information)
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-42442 (URN)10.1016/j.egypro.2017.12.626 (DOI)000452901602063 ()2-s2.0-85041530100 (Scopus ID)
Conference
9th International Conference on Applied Energy, ICAE 2017, 21 August 2017 through 24 August 2017
Available from: 2019-01-25 Created: 2019-01-25 Last updated: 2019-03-29Bibliographically approved
Maher, A. & Wallin, F. (2017). Finite State Machine Household's Appliances Models for Non-intrusive Energy Estimation. In: Energy Procedia: . Paper presented at 8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016 (pp. 2157-2162). Elsevier Ltd
Open this publication in new window or tab >>Finite State Machine Household's Appliances Models for Non-intrusive Energy Estimation
2017 (English)In: Energy Procedia, Elsevier Ltd , 2017, p. 2157-2162Conference paper, Published 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.

Place, publisher, year, edition, pages
Elsevier Ltd, 2017
Keywords
Appliances Modeling, Energy estimation, FSMs models, NILM, Energy utilization, Equipment, Dynamic fuzzy clustering, Finite state machine model, Management operation, Monitoring and control, Optimization approach, Transition probability matrix, Smart meters
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-36068 (URN)10.1016/j.egypro.2017.03.609 (DOI)000404967902042 ()2-s2.0-85020734215 (Scopus ID)
Conference
8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2018-07-25Bibliographically approved
Maher, A. & Wallin, F. (2017). Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden. Energy, 123, 108-118
Open this publication in new window or tab >>Multi objective particle swarm optimization of hybrid micro-grid system: A case study in Sweden
2017 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 123, p. 108-118Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD, 2017
Keywords
Hybrid micro-grid, DERs, Renewable energy, Multi-objective optimization, Particle swarm optimization, Sensitivity analysis
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-35309 (URN)10.1016/j.energy.2017.01.149 (DOI)000399510900009 ()2-s2.0-85011948998 (Scopus ID)
Available from: 2017-05-12 Created: 2017-05-12 Last updated: 2017-05-12Bibliographically approved
Frost, A. E. E., Maher, A., Li, H. & Wallin, F. (2017). Patterns and temporal resolution in commercial and industrial typical load profiles. Energy Procedia, 105, 2684-2689
Open this publication in new window or tab >>Patterns and temporal resolution in commercial and industrial typical load profiles
2017 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 2684-2689Article in journal (Refereed) Published
National Category
Engineering and Technology Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-37557 (URN)10.1016/j.egypro.2017.03.775 (DOI)000404967902123 ()2-s2.0-85020706627 (Scopus ID)
Available from: 2017-12-22 Created: 2017-12-22 Last updated: 2019-10-14Bibliographically approved
Maher, A. & Wallin, F. (2017). Smart meter data clustering using consumption indicators: responsibility factor and consumption variability. In: Yan, J Wu, J Li, H (Ed.), PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY: . Paper presented at 9th International Conference on Applied Energy (ICAE), AUG 21-24, 2017, Cardiff, ENGLAND (pp. 2236-2242). ELSEVIER SCIENCE BV
Open this publication in new window or tab >>Smart meter data clustering using consumption indicators: responsibility factor and consumption variability
2017 (English)In: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY / [ed] Yan, J Wu, J Li, H, ELSEVIER SCIENCE BV , 2017, p. 2236-2242Conference paper, Published 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.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV, 2017
Series
Energy Procedia, ISSN 1876-6102 ; 142
Keywords
Demand response, Smart metering, Consumers clustering, Responsibility factor, Variabiliy
National Category
Energy Engineering
Identifiers
urn:nbn:se:mdh:diva-42256 (URN)10.1016/j.egypro.2017.12.624 (DOI)000452901602061 ()2-s2.0-85041517474 (Scopus ID)
Conference
9th International Conference on Applied Energy (ICAE), AUG 21-24, 2017, Cardiff, ENGLAND
Available from: 2019-01-03 Created: 2019-01-03 Last updated: 2019-01-16Bibliographically approved
Maher, A. (2016). An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks. In: The 8th International Conference on Applied Energy – ICAE2016: . Paper presented at The 8th International Conference on Applied Energy – ICAE2016.
Open this publication in new window or tab >>An analyze of long-term hourly district heat demand forecasting of a commercial building using neural networks
2016 (English)In: The 8th International Conference on Applied Energy – ICAE2016, 2016Conference paper, Oral presentation with published abstract (Refereed)
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-34708 (URN)
Conference
The 8th International Conference on Applied Energy – ICAE2016
Available from: 2017-01-24 Created: 2017-01-24 Last updated: 2017-01-24Bibliographically approved
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