116 resultados para building cooling load


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This study discusses the significance of having service as a business logic, and more specifically, how value co-creation can be seen as an enhancing phenomenon to business-to-business relationships in traditional business sector. The purpose of this study is to investigate how value cocreation can enhance a business-to-business relationship in the heating, ventilation and airconditioning (HVAC) industry of building services engineering, through three sub-objectives: to identify what is value in the industry, how value is co-created in the industry, and what is value in a business-to-business relationship in the industry. The theoretical part this study consists of academic knowledge and literature related to the concepts of value, value co-creation and business-to-business relationships. In order to research value co-creation and business-to-business relationships in HVAC industry of building services engineering both, metaphorical and conceptual thinking of service dominant (S-D) logic and more managerial approach of service logic (SL), contributed to the theoretical part of the study. The empirical research conducted for this study is based on seven semi-structured interviews, which constituted the holistic, qualitative single case study method chosen for the research. The data was collected in September 2014 from CEOs, managers and owners representing six building services engineering firms. The interviews were analysed with the help of transcriptions, role-ordered matrices and thematic networks. The findings of this study indicate that value in HVAC industry consists of client expertise and supplier expertise. The result of applying client expertise and supplier expertise to the business-to- business relationship is characterized as value-in-reputation, when continuity, interaction, learning and rapport of the business relationship are ensured. As a result, value co-creation in the industry consists of mutual and separate elements, which the client and the supplier apply in the process, in addition to proactive interaction. The findings of this study, together with the final framework, enhance the understanding of the connection existing between value co-creation and business-to-business relationship. The findings suggest that value in the HVAC industry is characterized by both value-in-use and value-inreputation. Value-in-reputation enhances the formation of value-in-use, and consequently, value cocreation enhances the business-to-business relationship. This study thus contributes to the existing knowledge on the concepts of value and value co-creation in business-to-business relationships.

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Smart home implementation in residential buildings promises to optimize energy usage and save significant amount of energy simply due to a better understanding of user's energy usage profile. Apart from the energy optimisation prospects of this technology, it also aims to guarantee occupants significant amount of comfort and remote control over home appliances both at home locations and at remote places. However, smart home investment just like any other kind of investment requires an adequate measurement and justification of the economic gains it could proffer before its realization. These economic gains could differ for different occupants due to their inherent behaviours and tendencies. Thus it is pertinent to investigate the various behaviours and tendencies of occupants in different domain of interests and to measure the value of the energy savings accrued by smart home implementations in these domains of interest in order to justify such economic gains. This thesis investigates two domains of interests (the rented apartment and owned apartment) for primarily two behavioural tendencies (Finland and Germany) obtained from observation and corroborated by conducted interviews to measure the payback time and Return on Investment (ROI) of their smart home implementations. Also, similar measures are obtained for identified Australian use case. The research finding reveals that building automation for the Finnish behavioural tendencies seems to proffers a better ROI and payback time for smart home implementations.

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The growing population in cities increases the energy demand and affects the environment by increasing carbon emissions. Information and communications technology solutions which enable energy optimization are needed to address this growing energy demand in cities and to reduce carbon emissions. District heating systems optimize the energy production by reusing waste energy with combined heat and power plants. Forecasting the heat load demand in residential buildings assists in optimizing energy production and consumption in a district heating system. However, the presence of a large number of factors such as weather forecast, district heating operational parameters and user behavioural parameters, make heat load forecasting a challenging task. This thesis proposes a probabilistic machine learning model using a Naive Bayes classifier, to forecast the hourly heat load demand for three residential buildings in the city of Skellefteå, Sweden over a period of winter and spring seasons. The district heating data collected from the sensors equipped at the residential buildings in Skellefteå, is utilized to build the Bayesian network to forecast the heat load demand for horizons of 1, 2, 3, 6 and 24 hours. The proposed model is validated by using four cases to study the influence of various parameters on the heat load forecast by carrying out trace driven analysis in Weka and GeNIe. Results show that current heat load consumption and outdoor temperature forecast are the two parameters with most influence on the heat load forecast. The proposed model achieves average accuracies of 81.23 % and 76.74 % for a forecast horizon of 1 hour in the three buildings for winter and spring seasons respectively. The model also achieves an average accuracy of 77.97 % for three buildings across both seasons for the forecast horizon of 1 hour by utilizing only 10 % of the training data. The results indicate that even a simple model like Naive Bayes classifier can forecast the heat load demand by utilizing less training data.

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With the new age of Internet of Things (IoT), object of everyday such as mobile smart devices start to be equipped with cheap sensors and low energy wireless communication capability. Nowadays mobile smart devices (phones, tablets) have become an ubiquitous device with everyone having access to at least one device. There is an opportunity to build innovative applications and services by exploiting these devices’ untapped rechargeable energy, sensing and processing capabilities. In this thesis, we propose, develop, implement and evaluate LoadIoT a peer-to-peer load balancing scheme that can distribute tasks among plethora of mobile smart devices in the IoT world. We develop and demonstrate an android-based proof of concept load-balancing application. We also present a model of the system which is used to validate the efficiency of the load balancing approach under varying application scenarios. Load balancing concepts can be apply to IoT scenario linked to smart devices. It is able to reduce the traffic send to the Cloud and the energy consumption of the devices. The data acquired from the experimental outcomes enable us to determine the feasibility and cost-effectiveness of a load balanced P2P smart phone-based applications.

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Despite the complexity of the Chinese culture consumer product businesses should apply them in building brands for Chinese markets. There are reasons to believe that cultural values and attitudes affect the buying behavior of Chinese consumers. Companies that wish to create brands in China should therefore be aware of the prevailing cultural values and consumer attitudes. This thesis will examine which values and attitudes mostly affect Chinese consumers of health food products. The study will be done by conducting a netnography. Because netnography is actually a collection methods rather than a single method, other auxiliary methods will also be applied. These methods are emotion, language and sentiment analysis (ELS analysis). Emotion analysis will be conducted because cultural values are mostly built on emotional basis. Sentiment analysis will assist in recognizing the key factors that help to locate values and attitudes. Because the netnography will be conducted in Chinese web forums by a non-native researcher, linguistic aspects should also be analyzed in parallel with emotions and sentiment analysis. The study shows that the Chinese consumers of health food products put much importance on functional, analytical and collectivistic attitudes as well as social and psychological values. Of all the twelve cultural values defined, the role of family rose above all. Also perseverance, frugality, guanxi and harmony were highly presented. The attitudes were found by recognizing certain attitude factors. Of all the factors, health foods’ functional benefits and aesthetic content together with consumers’ value consciousness surpassed other factors. Besides these results that can be applied by foreign health food companies willing to enter Chinese consumer markets, also academia can benefit this new approach for conducting ethnographies online.