The goal of this master's thesis is to design and implement a system for recognition of home appliances based on their power consumption characteristics. This system should identify the individual home appliances from measurements of the total household consumption. The acquired data could be used for statistics of usage of a particular appliance and subsequent detection of errors or non-standard behavior of the measured device. An important part of my work is a design and hardware implementation of a unit for measuring and a system for processing the measured signal. The first version of my project uses pulse output of an electrometer to measure the energy. This method does not provide a sufficient sample rate but it's a quick way to obtain data for processing and analysis. The second version monitors the power consumption with a multi-purpose AC converter which measures active and reactive power with the desired sample rate. The data is then processed and recognized by two classifiers - HMM and KNN.

CMOS is well known for its ability to scale. This fact is reflected in the aggressive scaling on a continual basis from the invention of CMOS up to date. As devices are scaled, device performance improves due to shorter channel lengths and more densely packed functions for the same amount of area. In recent years, however, the performance gain obtained through scaling has begun to suffer under the degradation of the associate interconnect performance. As devices become smaller, interconnects need to follow. Unlike transistors, the scaling of interconnects results in higher capacitances and resistances, thereby limiting overall system performance. Trying to alleviate the delay effects results in increased power consumption, especially in global structures such as clock distribution networks. A possible solution to this problem is the use of optical interconnects, which are fast and much less lossy than the electrical equivalents. This dissertation describes an investigation on what future technology nodes will entail in terms of power consumption of clock networks, and what is required for an optical alternative to become feasible. A common clock configuration is used as a basis for comparison, where both electrical and optical networks are designed to component level. Optimisation is done on both to ensure a reasonable comparison, and the results of the respective power consumption components are then compared in order to find the criteria for a feasible optical clock distribution scheme. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

Data centre networks are currently experiencing a dramatic increase in the amount of network traffic that needs to be handled due to cloud technology and several emerging applications. To address this challenge, mega data centres are required with hundreds of thousands of servers interconnected with high bandwidth interconnects. Current data centre networks, based on electronic packet switches, consume a huge amount of power to support the increased bandwidth required by the emerging applications. Optical interconnects have gained more and more attentions as a promising solution offering high capacity and consuming much lower energy compared to the commodity switch based solutions. This thesis provides a thorough literature study on optical interconnects for data centre networks that are expected to efficiently handle the future traffic. Two major types of optical interconnects have been reviewed. One is referred to hybrid switching, where optical switching deals big flows while electronic switches handles traffic in packet level. The other one is based on all-optical switch, where power-consuming electronic interconnects can be completely avoided. Furthermore, the thesis includes a qualitative comparison of the presented schemes based on their main features such as topology, technology, network performance, scalability, energy consumption, etc.

There is seemingly a never-ending consumer demand for mobileterminals such as cellular phones and personal digitalassistants (PDAs). Each new generation of terminals comes withmore elaborate functions than in the previous generation. Thistrend results in a higher performance demand on the computerarchitecture that performs the required computations within theterminal. To satisfy the projected requirements on cominggenerations of mobile terminals, we propose an architecturethat when intelligently managed can provide the necessaryperformance at low power and energy consumption. Thisarchitecture, a chip-multiprocessor (CMP), thus amulti-processor implemented on a single chip, has incombination with adaptive scheduling strategies the potentialto efficiently fullfill future requirements. This licentiate thesis spans over several studies done onthe effectiveness of the adaptive CMP. In our studies, we haveshown that an adaptive CMP can satisfy the same performancerequirements as a comparable uni-processor, still consumingless power and energy. Furthermore, we have made an effort toaccurately model the workload behaviour of mobile terminals,which is of paramount importance when comparing candidatearchitectures. In the future, apart from proposing moreadaptive scheduling techniques, we expect to do more thoroughstudies on workload modeling as well as on the operating systeminfluence on the overall performance and power consumption.

The awareness of power consumption is considered to be an important step of driving energy- saving technologies development. With the ‘Smart Grid’ technologies, Sweden has deployed Advanced Metering Infrastructure (AMI) nationwide. How to present and operate the data be collected by AMI devices in an effective and friendly way has become a problem worth to research on. This thesis project aims to provide an energy consumption monitoring system, which integrate power consumption information from AMI, and geographic information from coordinates, for both customers and power companies, practical for the latter one. Based on that, active web map services of Geographic Information Systems (GIS) in Swedish market are also been analyzed in this thesis report, in order to investigate the features which may be applied in this system. The energy consumption monitoring system presented in this project is developed on the GIS of Google Maps, web-browser based. With the open Google Maps API for Javascript and WAMP environment support, a working system is given locally. In this project, at a designated Swedish area, Smedjebacken, there are 50 end-users with AMI data periodically recorded at the interval level of one hour for a whole year, and 3 substations connecting part of the end-users, all of them have the coordinates to position. AMI data is used to simulate a real-time scenario to test the monitoring function. Functions of this system are separated into two aspects, customers and power companies. Customers can check their power consumption in many different ways, including latest hour’s consumption, any past single day’s consumption, generate whole year’s consumption column chart, comparing with the average consumption level with a certain range of neighbors. Power companies have global view and access authorities of all customers and substations. In addition, an alarm will be triggered if any of the substation’s power loss is over 5%. As a result of analysis, the advantage lay with Google Map on cost of developing, open API and documentations. In the end, a working monitoring system is given under the simulated real-time power consumption scenario.

Ice rinks are among the most energy intensive public buildings in developed and developing countries. According to a research on Swedish ice rinks; a typical ice rink consumes approximately 1185 MWh/year which leads to more than 300 GWh/year for the 342 Swedish indoor ice rinks. The refrigeration system is usually the largest consumer by 43% average share of the total energy consumption.  To decrease the refrigeration system energy demand, there are a variety of energy efficiency techniques known and available but the key to select the best ones is finding the major heat loads on the ice sheet and refrigeration system, which is unique for each ice rink. To fulfil this objective and in addition to review literature, this study has two main approaches. The first approach is to measure and evaluate the performance of the refrigeration system in two ice rinks, called Norrtälje and Älta. The estimated cooling capacity is approximately equal to the total heat load on the ice plus the heat gains in the distribution system. This goal has been accomplished by using a performance analyser called “ClimaCheck” which is based on an “internal method” because it uses the compressor as an internal mass flow meter and consequently, there is no need for an external one. The refrigerant mass flow rate is calculated by an energy balance over the compressor. By knowing the mass flow, enthalpy of the refrigerant, etc. the cooling capacity and COP of the system can be calculated. While the total heat load is known by the first approach, the second approach tries to discover different heat loads shares by analytical modelling. The measured physical and thermodynamical parameters plus the ice rink geometrical characteristics are input to the heat transfer correlations to estimate the heat load magnitude. The results of the measurements show that the total energy consumption in Norrtälje is about two third of Älta. The main reasons for this less energy consumption are smarter control systems for compressors and pumps, better ventilation distribution design and 1°C-2°C higher ice temperature.      Analytical modelling for a sample day has estimated that about 84% of the total heat loads is originated from the heat loads on ice sheet while the distribution system causes the remaining 16%. Moreover, calculations show that convection plus small portion of condensation (altogether 36%), radiation (23%), ice resurfacing (14%) and lighting (7%) are the largest heat loads in winter while in summer condensation is another significant heat load (10%). Comparing two six-hour periods, one without ice resurfacing and four resurfacings in the second one, 30% more cooling demand has been calculated for the second period. Furthermore, it has been shown that the evaporator to brine is the contributor for 66% of the heat transfer resistances from ice to evaporator while brine to bottom ice and bottom to top ice accounts for 27% and 7% respectively. To conclude, a parallel “performance analysis of the refrigeration system” and “heat loads estimation” proves to be a useful tool for adopting proper design and control for energy efficient operation. / Stoppsladd financed by Swedish Energy Agency (Energimyndigheten) and Swedish Ice Hockey Association

The climate change has been an ongoing debate throughout the years. There are already some devices that monitor the changing of the climate, CanSat is a good example. The main goal of this project is to create a CanSat prototype and analyse the power used by it. The focus is on several factors such as the use of power with and without an upload program, the use of power when the sensors are switched on/off and the duration of the battery using the prototype. Some parts of the analysis have been done theoretically and practical. The project has been conducted with the aid of Arduino, an ammeter, and a voltmeter. The results show that the prototype would not spare much power if the sensors are switched off and on, this because not all the sensors have implemented the “sleep mode”. The difference between sleep mode and the normal functionality is equal to 0.026𝑊. Moreover, the difference in power when there is an upload program and when there is not an upload program is equal to 0.057𝑊. The duration of battery in the prototype is equal to 1 hour and 45 minutes according to the theoretical part, while the practical part showed a duration of 1 hour and 11 minutes. Moreover, the results show also that the prototype send the wrong values for some of the sensors when the battery have a low value. It was estimated a value of 7V of battery left to guarantee credible measurements. From the result it is possible to deduce that the decrease of power used from the CanSat prototype can be improved by finding sensors that have implemented the sleep mode, by having a small code and by having an electric platform that consume less power than Arduino. Keywords: CanSat, Power consumption, Battery life, Arduino.

In 1982, the Bonneville Power Administration (BPA), a marketer of hydroelectric power in the Pacific Northwest, found itself in a new role which required it to acquire power resources needed to meet the demands of the region's utilities. In particular, it had to deal with the Washington Public Power Supply System's nuclear plant cost escalations. In response, BPA prepared its first independent regional power forecast. The forecast development process was intricate and multidimensional and involved a variety of interested parties. Application of the Multiple Perspective Concept uncovers strengths and weaknesses in this process by illuminating its technical, organizational and personal dimensions. Examination of the forecast from the technical perspective revealed an elaborate set of interlinked models used to develop baseline, high, and low forecasts. The organizational perspective revealed BPA to be in a transitional stage. Internally, ratemaking, forecasting, conservation, resource acquisition, and financial management swelled as new organizational functions. Interorganizationally, environmentalists, ratepayer groups, and the region's utilities all had strong interests in the decision regarding WPPSS plants. The personal perspective revealed that each of the Administrators heading BPA since the early 1980s defined the agency's approach to the resource planning problem differently, first as an engineering problem, then as a political problem, and, finally, as a business problem. Taken together, the Multiple Perspectives yielded the following conclusions about BPA's 1982 forecast. (1) BPA's range forecast constituted a major improvement over the point forecasts preceding it, but left important classes of uncertainty unexplored. (2) BPA's models were better suited to address rate and conservation issues important at the time of the 1982 forecast than their predecessors. The model of the national economy, however, remained a black box, potentially significant feedbacks were not represented, and the sheer size of the modeling system placed practical limits on its use. (3) A stronger method of dealing with forecast uncertainty is needed which utilizes a disaster-avoidance strategy and plans for high impact/low probability events. This method need not involve the use of large models, but should incorporate qualitative insights from persons normally outside the technical sphere.

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Internet of Things (IoT) introduces the Big Data era as the IoT devices produce massive amounts of data daily. Since IoT devices contain limited computational and processing capabilities, processing the data at the edge is challenging. For example, power consumption becomes problematic if data is processed on the IoT device itself. Thus, there is a need to feed this massive data into the cloud platform for analysis. However, uploading the data from IoT devices to the cloud platform causes a delay which is a significant issue for delay-sensitive applications. This tradeoff between delay and power needs a favorable policy to decide where it should allocate the task from edge to cloud processing platform. Research on this subject addresses this issue quite frequently, and various methods have been proposed to mitigate the problem. The previous studies usually focus on the edge-to-cloud computing platform, i.e., they efficiently offload the computational tasks onto the IoT devices and cloud. This thesis proposes a balanced task allocation between edge and cloud computing regarding power consumption and delay. We accomplish our idea by comparing the different task allocation methods, benchmarking in different scenarios, and evaluating by proposing mathematical modeling.