Knivsta municipality is planning a new climate-friendly and environmentally sustainable district called Nydal. The vision is to create a residential area that is minimally dependent on outside sources of electricity. The aim of this research is to evaluate the efficiency and electricity demand of a residential house in Knivsta. A model for the power demand of an apartment house has been constructed, using data for the electricity consumption of one household from Kiruna during a week in April. Data from the Swedish Energy Agency has been used to identify the distribution of electricity for the different loads. This study has focused on the electricity consumption of a family that consist of two adults and one child and their use of home appliances. A comparison between standard appliances and energy efficiency appliances has been made to find out how much the power and their load peaks can be reduced. The total amount of electricity consumption for the residential building’s households is about 93 MWh during a year. By selecting energy efficient appliances in the apartments the total household electricity consumption of the residential building can be reduced by about 55 MWh, which is almost a 60 % reduction per year. The residential buildings load peaks can be reduced. This can be achieved by installing energy efficient products by using Top Ten’s list over the most energy saving household appliances available on the market. Installing LED lamps and HWC driven products also reduces the power demand significantly. By installing HWC driven products in the common washhouse, the residential electricity can be reduced by 29 %. A more environment-friendly lifestyle affects the electric load. The power demand can decrease if buying less power demanding products, but also by using the power demanding products in a more energy efficient way. Knivsta municipality is able to help the tenants in Nydal to increase their knowledge. By increasing the individuals’ knowledge about power demand and energy saving, it is possible to influence individuals’ behavior and with that reduce the electric load.

Efficient control is often the most cost-effective option to improve on the running cost of a Three-Pipe Water Pumping System. However, the effect of changing the control strategy (i.e. on energy consumption) is usually difficult to predict. To obtain this information more easily, a new simulation tool, QUICKcontrol, was developed. This new tool was used to investigate the energy cost savings potential in a Three-Pipe Water Pumping System. The influence of pump scheduling, dam level set points, control parameters and different combinations thereof was investigated. The simulation models were firstly verified with measurements obtained from the existing system to confirm their accuracy for realistic control retrofit simulations. With the aid of the integrated simulation tool it was possible to predict savings of R 195'000 per year with an average 3.8 MW of load shifted. / Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2005.

Efficient control is often the most cost-effective option to improve on the running cost of a Three-Pipe Water Pumping System. However, the effect of changing the control strategy (i.e. on energy consumption) is usually difficult to predict. To obtain this information more easily, a new simulation tool, QUICKcontrol, was developed. This new tool was used to investigate the energy cost savings potential in a Three-Pipe Water Pumping System. The influence of pump scheduling, dam level set points, control parameters and different combinations thereof was investigated. The simulation models were firstly verified with measurements obtained from the existing system to confirm their accuracy for realistic control retrofit simulations. With the aid of the integrated simulation tool it was possible to predict savings of R 195'000 per year with an average 3.8 MW of load shifted. / Thesis (M.Ing. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2005.

Submitted by Thays Aparecida de Abreu Silva null (thays7abreu@gmail.com) on 2016-08-24T12:25:29Z No. of bitstreams: 1 Thays - Final03-08-16.pdf: 1979884 bytes, checksum: 531230641c5b2eb640f10441ac13f396 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-08-25T13:57:50Z (GMT) No. of bitstreams: 1 santos_taa_dr_ilha.pdf: 1979884 bytes, checksum: 531230641c5b2eb640f10441ac13f396 (MD5) / Made available in DSpace on 2016-08-25T13:57:50Z (GMT). No. of bitstreams: 1 santos_taa_dr_ilha.pdf: 1979884 bytes, checksum: 531230641c5b2eb640f10441ac13f396 (MD5) Previous issue date: 2016-06-09 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Considerando a crescente demanda de energia elétrica no setor residencial, faz-se necessário conhecer o padrão de consumo de eletricidade de forma detalhada, impulsionando a mudança do comportamento dos consumidores finais, com o objetivo de reduzir o consumo global e a racionalização do uso da energia elétrica. Portanto, conhecer o perfil da curva de carga, com antecedência, é importante para detectar os picos e os vales, e incentivar os consumidores a mudar seus hábitos de consumo de energia, principalmente durante os períodos em que as tarifas são mais caras. Assim, nesta pesquisa propõe-se a utilização de um sistema fuzzy para obter o perfil de carga elétrica residencial. Como o consumo de energia elétrica, em residências, está altamente correlacionado com a ocupação ativa, foram levados em consideração o número de ocupantes na residência e os diferentes períodos do dia ao longo de 24 horas. Com base neste modelo foi possível simular o perfil de carga elétrica, a detecção dos picos que podem comprometer a eficiência do sistema e, consequentemente, oferecer mecanismos para melhorar o gerenciamento de demanda e incentivar a utilização racional de energia elétrica. Com objetivo de verificar a eficiência do sistema fuzzy, comparou-se as curvas de carga obtidas pelo sistema proposto com as curvas de carga reais e por meio desta comparação foi possível observar que os resultados são promissores. / The electrical energy demand is increasing mainly in residences. Therefore, it is necessary to know in advance the electricity pattern consumption. This knowledge is important to change behavior and reduce the global consumption. Furthermore, the load curve profile known in advance can detect the highest points and valleys and force the consumers to change their behavior principally during the high tariffs. Thus, this work proposes a fuzzy system to obtain the electrical load profile in residences. The electrical energy consumption is correlated with the active occupation of the residences, therefore the system considers the quantity of inhabitants and the different periods of the day during 24 hours. Based on this parameters it is possible to obtain the electrical load profile detecting the highest points that can compromise the efficiency of the system, and provide mechanisms to improve the demand managing besides forcing the rational use of electrical energy. To verify the efficiency of the proposed system, the results obtained are compared with real load curves measured in loco concluding that these results are promising.

Orientador: Anna Diva Plasencia Lotufo / Resumo: Considerando a crescente demanda de energia elétrica no setor residencial, faz-se necessário conhecer o padrão de consumo de eletricidade de forma detalhada, impulsionando a mudança do comportamento dos consumidores finais, com o objetivo de reduzir o consumo global e a racionalização do uso da energia elétrica. Portanto, conhecer o perfil da curva de carga, com antecedência, é importante para detectar os picos e os vales, e incentivar os consumidores a mudar seus hábitos de consumo de energia, principalmente durante os períodos em que as tarifas são mais caras. Assim, nesta pesquisa propõe-se a utilização de um sistema fuzzy para obter o perfil de carga elétrica residencial. Como o consumo de energia elétrica, em residências, está altamente correlacionado com a ocupação ativa, foram levados em consideração o número de ocupantes na residência e os diferentes períodos do dia ao longo de 24 horas. Com base neste modelo foi possível simular o perfil de carga elétrica, a detecção dos picos que podem comprometer a eficiência do sistema e, consequentemente, oferecer mecanismos para melhorar o gerenciamento de demanda e incentivar a utilização racional de energia elétrica. Com objetivo de verificar a eficiência do sistema fuzzy, comparou-se as curvas de carga obtidas pelo sistema proposto com as curvas de carga reais e por meio desta comparação foi possível observar que os resultados são promissores. / Abstract: The electrical energy demand is increasing mainly in residences. Therefore, it is necessary to know in advance the electricity pattern consumption. This knowledge is important to change behavior and reduce the global consumption. Furthermore, the load curve profile known in advance can detect the highest points and valleys and force the consumers to change their behavior principally during the high tariffs. Thus, this work proposes a fuzzy system to obtain the electrical load profile in residences. The electrical energy consumption is correlated with the active occupation of the residences, therefore the system considers the quantity of inhabitants and the different periods of the day during 24 hours. Based on this parameters it is possible to obtain the electrical load profile detecting the highest points that can compromise the efficiency of the system, and provide mechanisms to improve the demand managing besides forcing the rational use of electrical energy. To verify the efficiency of the proposed system, the results obtained are compared with real load curves measured in loco concluding that these results are promising. / Doutor

Šiame darba nagrinėjamos gyvenamųjų namų elektros apkrovos, elektros energijos suvartojimas. Pateikiami elektros apkrovų grafikai. Nagrinėjama kaip projektinė skaičiuojamoji galia keičiasi 47 metų laikotarpyje. Siūlomi būdai kaip nustatyti buto, namo skaičiuojamąsias apkrovas. Tiriama kiek realios namo skaičiuojamosios apkrovos skiriasi nuo projektinių. / This thesis addresses the residential electric load power consumption. Schedule of electrical loads. The present computational power of design changes 47-year period. Suggest ways to identify flat house computational loads. Investigated as far as the actual building computational load is different from the design.

The electric power grid is evolving in terms of generation, transmission and distribution network architecture. On the generation side, distributed energy resources (DER) are participating at a much larger scale. Transmission and distribution networks are transforming to a decentralized architecture from a centralized one. Residential and commercial buildings are now considered as active elements of the electric grid which can participate in grid operation through applications such as the Demand Response (DR). DR is an application through which electric power consumption during the peak demand periods can be curtailed. DR applications ensure an economic and stable operation of the electric grid by eliminating grid stress conditions. In addition to that, DR can be utilized as a mechanism to increase the participation of green electricity in an electric grid. The DR applications, in general, are passive in nature. During the peak demand periods, common practice is to shut down the operation of pre-selected electrical equipment i.e., heating, ventilation and air conditioning (HVAC) and lights to reduce power consumption. This approach, however, is not optimal and does not take into consideration any user preference. Furthermore, this does not provide any information related to demand flexibility beforehand. Under the broad concept of grid modernization, the focus is now on the applications of data analytics in grid operation to ensure an economic, stable and resilient operation of the electric grid. The work presented here utilizes data analytics in DR application that will transform the DR application from a static, look-up-based reactive function to a dynamic, context-aware proactive solution. The dynamic demand response framework presented in this dissertation performs three major functionalities: electrical load forecast, electrical load disaggregation and peak load reduction during DR periods. The building-level electrical load forecasting quantifies required peak load reduction during DR periods. The electrical load disaggregation provides equipment-level power consumption. This will quantify the available building-level demand flexibility. The peak load reduction methodology provides optimal HVAC setpoint and brightness during DR periods to reduce the peak demand of a building. The control scheme takes user preference and context into consideration. A detailed methodology with relevant case studies regarding the design process of the network architecture of a deep learning algorithm for electrical load forecasting and load disaggregation is presented. A case study regarding peak load reduction through HVAC setpoint and brightness adjustment is also presented. To ensure the scalability and interoperability of the proposed framework, a layer-based software architecture to replicate the framework within a cloud environment is demonstrated. / Doctor of Philosophy / The modern power grid, known as the smart grid, is transforming how electricity is generated, transmitted and distributed across the US. In a legacy power grid, the utilities are the suppliers and the residential or commercial buildings are the consumers of electricity. However, the smart grid considers these buildings as active grid elements which can contribute to the economic, stable and resilient operation of an electric grid. Demand Response (DR) is a grid application that reduces electrical power consumption during peak demand periods. The objective of DR application is to reduce stress conditions of the electric grid. The current DR practice is to shut down pre-selected electrical equipment i.e., HVAC, lights during peak demand periods. However, this approach is static, pre-fixed and does not consider any consumer preference. The proposed framework in this dissertation transforms the DR application from a look-up-based function to a dynamic context-aware solution. The proposed dynamic demand response framework performs three major functionalities: electrical load forecasting, electrical load disaggregation and peak load reduction. The electrical load forecasting quantifies building-level power consumption that needs to be curtailed during the DR periods. The electrical load disaggregation quantifies demand flexibility through equipment-level power consumption disaggregation. The peak load reduction methodology provides actionable intelligence that can be utilized to reduce the peak demand during DR periods. The work leverages functionalities of a deep learning algorithm to increase forecasting accuracy. An interoperable and scalable software implementation is presented to allow integration of the framework with existing energy management systems.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.

Thesis (Ph.D. (Mechanical Engineering))--North-West University, Potchefstroom Campus, 2008.