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  • 1.
    Idowu, Samuel
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hagos, Desta Haileselassie
    Tesfay, Welderufael Berhane
    Famurewa, Abiola
    Rana, Juwel
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Synnes, Kåre
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    NexTrend: Context-aware music-relay corridors using NFC tags2013In: 7th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing: IMIS 2013, Taichung, Taiwan; 3 July 2013 - 5 July 2013, Piscataway, NJ: IEEE Computer Society Press , 2013, p. 573-578Conference paper (Refereed)
    Abstract [en]

    The rise of pervasive computing presents unique opportunities due to increasing availability of smart devices such as mobile phones and tablets equipped with various sensors enabling Near Field Communication (NFC) technologies. The growth of mobile computing has led to an increase in access to digital music. With the growth of digital music, the development of music information sharing services for users becomes important. The existing sharing methods are based on the users’ social network and preferences in music. However, sometimes, sharing music according to location and time is needed.This paper presents work on smart spaces equipped with NFC tags, deployed at different locations in hallways for discovering and sharing new music experiences. This concept provides a new way of interaction between passers-by for discovering music in relation to location. For example, the hallway locations use sensing devices to provide an automatic means of exchanging music information among the passers-by.We utilized NFC tags as Music-Relay hot spots. The hot spot retrieves information about the music a user is playing on her/his device while s/he is passing by the hot spot. The work contributes to a pervasive service that equips an environment with music context intelligence about a passer-bys choice of music and allows users to feel the musical presence of other users who have been in the same location at previous point in time. In general, this paper proposes a new music information sharing service using the music information captured from users at a specific location in time.

  • 2.
    Idowu, Samuel O.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Applied Machine Learning in District Heating System2018Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption.

    This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings.

    The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine,  feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.

  • 3.
    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, p. 478-488Article 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.

  • 4.
    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.
    Forecasting Heat Load for Smart District Heating Systems: A Machine Learning Approach2014In: 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm 2014): Venice, 3-6 Nov. 2014, Piscataway, NJ: IEEE Communications Society, 2014, p. 554-559Conference paper (Refereed)
    Abstract [en]

    The rapid increase in energy demand requires effective measures to plan and optimize resources for efficient energy production within a smart grid environment. This paper presents a data driven approach to forecasting heat load for multi- family apartment buildings in a District Heating System (DHS). The forecasting model is built using six and eleven weeks of data from five building substations. The external factors and internal factors influencing the heat load in substations are parameters used as our model’s input. Short-term forecast models are generated using four supervised Machine Learning (ML) techniques: Support Vector Regression (SVR), Regression Tree, Feed Forwards Neural Network (FFNN) and Multiple Linear Regression (MLR). Performance comparison among these ML methods was carried out. The effects of combining the internal and external factors influencing heat load at substations was studied. The models are evaluated with varying horizon up to 24-hours ahead. The results show that SVR has the best accuracy of 5.6% MAPE for the best-case scenario.

  • 5.
    Idowu, Samuel
    et al.
    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.
    Machine learning in district heating system energy optimization2014In: 2014 IEEE International Conference on Pervasive Computing and Communications workshops: PERCOM WORKSHOPS 2014, Budapest, Hungary; 24-28 March 2014, Piscataway, NJ: IEEE Communications Society, 2014, p. 224-227, article id 6815206Conference paper (Refereed)
    Abstract [en]

    This paper introduces a work in progress, where we intend to investigate the application of Reinforcement Learning (RL) and online Supervised Learning (SL) to achieve energy optimization in District-Heating (DH) systems. We believe RL is an ideal approach since this task falls under the control-optimization problem where RL has yielded optimal results in previous work. The magnitude and scale of a DH system complexity incurs the curse of dimensionalities and model, hereby making RL a good choice since it provides a solution for the problem. To assist RL even further with the curse of dimensionalities, we intend to investigate the use of SL to reduce the state space. To achieve this, we shall use historical data to generate a heat load sub-model for each home. We believe using the output of these sub-models as feedback to the RL algorithm could significantly reduce the complexity of the learning task. Also, it could reduce convergence time for the RL algorithm. The desired goal is to achieve a realtime application, which takes operational actions when it receives new direct feedback. However, considering the dynamics of DH system such as large time delay and dissipation in DH network due to various factors, we hope to investigate things such as the appropriate data sampling rate and new parameters / sensors that could improve knowledge about the state of the system, especially on the consumer side of the DH network.

  • 6.
    Idowu, Samuel
    et al.
    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.
    Brännström, Robert
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Machine Learning in Pervasive Computing2013Report (Other academic)
    Abstract [en]

    Increase in data quantities and number of pervasive systems has resulted in many decision-making systems. Most of these systems employ Machine Learning (ML) in various practical scenarios and applications. Enormous amount of data generated by sensors can be useful in decision-making systems. The rising number of sensor driven pervasive systems presents interesting research areas on how to adapt and apply existing ML techniques effectively to the domain of pervasive computing. In the face of data deluge, ML has proved viable in many application areas such as data mining and self-customizing programs and could bring about great impact in the field of pervasive computing.The objective of this study is to give the underlying concepts of ML techniques that can be applied to problems in the domain of pervasive and mobile computing. The scope of this study covers the three primary types of ML, supervised, unsupervised and reinforcement learning methods. In the process of providing the fundamental knowledge of ML, we present some conceptual terms of ML and the steps required in developing ML system with a great impact on domains outside ML scope.Our findings show that previous works in the area of ubiquitous computing have successfully applied supervised learning and reinforcement learning methods. Hence, this study focuses more on supervised learning and reinforcement learning. In conclusion, we discuss some basic performance evaluation metrics and methods for obtaining reliable classifiers estimates, such as cross-validation and leave-one-out validation.

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