The building was selected for a detailed study of heating consumption that is located in the city of Gävle and the top fifth energy consumption in this city in 2006. Classrooms, workshops, offices and a restaurant with kitchen compose the building of two floors.The aim of this thesis is to design the best approach to reduce consumption and achieve a high efficiency of energy utilization in these companies. The project is going to optimize the system.Series of measurements are taken to achieve the heat losses. The heat losses are calculated through the building in the first step. Afterwards, with the result of ventilation, heating and electrical usage an energy balance is made to calculate the efficiency of the installation through the building envelop.It is to study the indoor climate within a building, as well as energy consumption for the entire building. In addition, it is also used to measure the temperature of ventilation systems and check the schedule of air supply.Analyze the result of the value that is measured. Improve some part of this building that reduces the heating consumption.At last give some suggestion like construction a new roof reduce the heat loss and change some door that is not correct in the building.

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Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.

Thesis (Ph. D.)--Open University. BLDSC no. DXN088257.

With the increasing demand for energy throughout the world, the environment around us is getting severely affected. The conventional energy sources (coal, oil and gas) are unfortunately the biggest polluters of the environment. The aforementioned energy sources emit greenhouse gases like carbon dioxide and methane, which are responsible for global warming and ozone layer depletion. The only feasible answer to this problem is to reduce the use of the conventional energy sources and focus more on other energy sources. The renewable energy sources (solar, wind and hydro) have been present in nature, but the technology to harvest these energies have always been relatively expensive until recently. The biggest advantage of using renewable energy sources is the fact that these energy sources will never run out and they also do not pollute the environment as their more conventional counterparts. With more research being conducted into better ways of storing the power trapped from the renewable energy sources and the relative difficulty of obtaining the ever-depleting conventional energy sources, the future for renewable energy sources definitely looks better than at any time in the past

No description available.

Wind energy systems are being closely studied because of its benefits as an environmentally friendly and renewable source of energy. Because of its unpredictable availability, power management concepts are essential to extract as much power as possible from the wind when it becomes available. The purpose of this thesis is to presents a new adaptive control algorithm for maximum power point tracking (MPPT) in wind energy systems. The proposed control algorithm allows the generator to track the optimal operation points of the wind turbine system under fluctuating wind conditions and the tracking process speeds up over time. This algorithm does not require the knowledge of intangible turbine mechanical characteristics such as its power coefficient curve, power characteristic or torque characteristic. The algorithm uses its memory feature to adapt to any given wind turbine and to infer the optimum rotor speeds for wind speeds that have not occurred before. The proposed algorithm uses a modified version of Hill Climb Search (HCS) and intelligent memory to implement its power management scheme. This algorithm is most suitable for smaller grid or battery connected wind energy systems. PSIM simulation studies have been done to confirm the effectiveness of the proposed algorithm. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-12-15 20:47:44.884

No description available.

Thesis (masters)--Montana State University. / Includes bibliographical references.

Thesis--Ethnikon Metsovion Polytechneion, 1979. / Summaries in Greek and English. Includes bibliographical references (p. 303-310).