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The purpose of this thesis is to assist the Swedish energy company NIBE Energy Systems in their studies of adaptive regulation applicable to electrical water heaters. Due to coming energy classifications of these appliances in the European Union, NIBE Energy Systems needs to use adaptive regulation, called Smart Control, to keep their products in the best possible energy class and remain competitive to the market. By using this Smart Control regulation a 2-3 % improve-ment of efficiency can be credited the system. This is a small number, but heavily needed, since the energy classes are based on the idea that the European Union is provided with electricity from coal condensate power resulting in a 40 % maximum efficiency. Furthermore, doing noth-ing will result in some water heaters not being approved to use on the market from 2015 due to low efficiency. The thesis is made out of three sections; product-/literature studies of products already commercially available using similar principles, data analysis on existing Smart Controlled water heater and recommendations to the company for future development of their own system. Limitations have been made through simplified calculations and thermodynamic assumptions. A conclusion can still be made from the thesis; electrical water heaters using Smart Control saves 10 - 15 % of electrical energy use today in a real world environment. Main sources to this thesis have been data analysis, Internet, brochures and conversations with the mentors. / Syftet med det här examensarbetet är att assistera det svenska energiföretaget NIBE i deras stu-dier av adaptiv reglering beträffande företagets sortiment av varmvattenberedare. På grund av kommande energiklassificering av dessa inom EU behöver NIBE Energy Systems använda en adaptiv reglering kallad Smart Control, för att placera deras produkter i bästa möjliga energiklass och fortsätta vara konkurrenskraftiga på marknaden. Genom att använda denna Smart Control får 2-3 % verkningsgrad tillgodoräknas systemet. Detta kan tyckas vara ett litet tillskott men är istället mycket nödvändigt. Eftersom energiklasserna är baserade på tanken att hela EU förses med el från kolkondenskraftverk, resulterar det i en teoretisk maximal verkningsgrad av 40 %. Görs ingenting blir vissa varmvattenberedare ej godkända att sälja redan år 2015. Rapporten består av tre delar; produkt-/litteraturstudie av varor som redan finns kommersiellt tillgängliga och använder liknande principer, dataanalys av ett existerande system som styrs av Smart Control och rekommendationer till företaget för framtida utveckling av ett eget system. Avgränsningar har gjorts genom att till viss del förenkla och anta värden i beräkningar och termodynamisk teori. En slutsats kan trots detta dras i arbetet; eluppvärmda varmvattenberedare som använder sig av en Smart Control sparar i dagsläget 10 - 15 % elenergi i en verklig miljö. Huvudsakliga källor i arbetet har varit mätdataanalys, Internet, broschyrer och konversationer med handledarna.

High electricity prices have become the title of several news articles recently in Sweden and the prices have experienced large sudden fluctuations during certain periods. In this thesis work, a smart control model for the electricity usage in three different households has been developed with the main purpose to minimize the electricity cost. This has been implemented by using mixed-integer linear programming (MILP) to optimize the cost 24 hours ahead, and by forecasting two of the main inputs; the load and the electricity spot prices for bidding zone three (SE3) in Sweden. The units included in the model are the photovoltaic system, the batteries, the electricity consumption in the house and the electric vehicles. However, the main task of the smart control was to determine when and in which amount the energy should flow from one unit to another, or to/from the grid. In other words, it decides the charging/discharging of the batteries, the selling/buying of electricity and the charging of the electric vehicle (EV). Different amounts of cost savings/profits have been obtained when applying the smart control on the three houses, which have different annual consumption, capacities of the components, heating systems and more. The results showed that it is most optimal to run the model between the time interval 13.00-00.00, when the spot prices for the next day are known, in order to avoid the remarkable impact accompanied with the use of forecasted electricity prices as input to the model. The forecasting of the load is, on the other hand, required to run the model, but this thesis showed that the effect of the uncertainties in this forecast is relatively small. Three types of machine learning methods were implemented to perform the forecasts, namely linear regression (LR), decision tree regression and random forest regression. After measuring especially the mean absolute error (MAE) to validate the results, the random forest regression showed the least error and the other methods showed close results when looking at the electric load prognosis.

According to the U.S. energy consumption survey in 2012, about 25% of the commercial and 42% of the residential building energy were used for heating. Despite the development of new and more efficient Heating, Ventilation, and Air Conditioning (HVAC) systems over the years, the high energy consumption in heating is still one of the major energy efficiency issues. Studies showed that decreasing HVAC operating temperature set points by 4°F will result in energy savings of 15% or more. Thus, the smart localized heating control (SLHC) system was designed and prototyped to provide localized heat directly to a person so that HVAC can run at a lower temperature set point. SLHC detects human movement and delivers the heat based on the result of the target location estimation and temperature measurement feedback. To detect the human movement, image processing techniques were used; image segmentation, mass center detection, background subtraction using the Mixture of Gaussian model, and human feature detection. In SLHC, a near-infrared heater and a tracking function were used to provide an instant and a direct heat to the person in order to minimize wasting energy. The SLHC system is divided into the sensing and processing (SP) and the heating and regulating (HR) subsystem. The SP’s primary function is to process captured video images and measured temperature data. SP also generates and sends the heater operating signal to HR. HR purposes to control the heater’s direction and power based on the signal. The communication between SP and HR was established through Wi-Fi enabled development platform. The SLHC prototype successfully processed the sensing data and transmitted the control signal. The result shows that it detected human movement and estimated the person’s location in 3D space within 10% margin of error. Also, it delivered the focused heat to the surface of the human body and increased the temperature by 10.0°F in 3 minutes at the distance of 1.5m away from the heater. This cost-effective, wireless, and localized heating system demonstrates the potential to improve energy efficiency in buildings. / Electrical and Computer Engineering