Energy issues in the building sector become more and more important nowadays. Although the technology improves, the energy consumption remains the same because of people’s way of living. To reduce the energy consumption, it is possible to improve the technical components that form the building envelope and to change people’s habits. This report aims at determining the best measurement methodology of the heating and hot water consumption of a building to insure real-time visualization and evaluating the energy savings that could be made by changing people habits. To do so, an existing measurement methodology is analyzed by making error calculations and computer-based modeling and simulations are carried out to determine the heating consumption of the building under different conditions. The program DesignBuilder is used to assess the energy consumption of the building. The study shows that a consequent reduction of the heating consumption is possible by only changing people’s habits. Real-time visualization would be really helpful but it needs very accurate measurements that are almost impossible if they are not integrated in the first stages of the building process.

The doctoral thesis is describing the problematic of motion parameters influences on energy consumption during robot operation. The basic methods of robot programming are characterized and evaluated in the introductory part and with help of mathematical models the influence of specific parameters is explained. The experimental verification of mathematical models was performed in the laboratory of Institute of Production Machines, Systems and Robotics at Brno University of Technology. The measure results are arranged in tables from which final evaluation and recommendation for praxis follows.

This thesis is a part of the master's in data science course at LTU. The core objective would be to build models that can do a short-term prediction of electricity energy consumption based on historical consumption data. With the increasing demand for electricity, forecasting electricity consumption is important and must be more accurate and closer to the actual values. As a part of this thesis, three different time series forecasting models are studied and experimented. The first model is based on an ensemble of Facebook prophet and XGBoost models together, the second model is based on deep learning neural network using Long short-term memory a Recurrent Neural Network, the third model is based on Convolution neural network. The performance of these three models is discussed and improvements needed, are also mentioned.These three models are trained with data from 2014-2019 and predictions are evaluated with 2020. As 2020 is the core of the COVID-19 pandemic season, offices were closed and this has impact on the model performance and evaluations. These impacts are also highlighted. Cross Industry standard process for Data mining methodology is followed in this thesis.

From the earliest and simplest scalar computation engines to modern superscalar out-oforder processors, the evolution of computational machinery during the past century has largely been driven by a single goal: performance. In today’s world of cheap, billion-plus transistor count processors and with an exploding market in mobile computing, a design landscape has emerged where energy efficiency, arguably more than any other single metric, determines the viability of a processor for a given application. The historical emphasis on performance has left modern processors bloated and over provisioned for everyday tasks in the hope that during computationally intensive periods some performance improvement will be observed. This work explores an energy-efficient processor design technique that ensures even a highly over provisioned out-of-order processor has only as many of its computational resources active as it requires for efficient computation at any given time. Specifically, this paper examines the feasibility of a dynamically banked register file and reorder buffer with variable banking policies that enable unused rename registers or reorder buffer entries to be voltage gated (turned off) during execution to save power. The impact of bank placement, turn-off and turn-on policies as well as rail stabilization latencies for this approach are explored for high-performance desktop and server designs as well as low-power mobile processors

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In recent years, we have seen the applications of distributed autonomous mobile robots (DAMRs) in a broad spectrum of areas like search and rescue, disaster management, warehouse, and delivery systems. Although each type of systems employing DAMRs has its specific challenges, they are all limited by energy since the robots are powered by batteries which have not advanced in decades. This motivates the development of energy efficiency for such systems. Although there has been research on optimizing energy for robotic systems, their approaches are from low-level (e.g., mechanic, system control, or avionic) perspectives. They, therefore, are limited to a specific type of robots and not easily adjusted to apply for different types of robots. Moreover, there is a lack of work studying the problem from a software perspective and abstraction. In this thesis, we tackle the problem from a software perspective and are particularly interested in DAMR systems in which a team of networked robots navigating in a physical environment and acting in concert to accomplish a common goal. Also, the primary focus of our work is to design schedules (or plans) for the robots so that they can achieve their goal while spending as little energy as possible. To this end, we study the problem in three different contexts: - Managing reliability and energy consumption tradeoff. That is, we propose that robots verify computational results of one another to increase the corroboration of outputs of our DAMR systems. However, this new feature requires robots to do additional tasks and consume more energy. Thus, we propose approaches to reach a balance between energy consumption and the reliability of results obtained by our DAMR systems. - Extending the operational time of robots. We first propose that our DAMR systems should employ charging stations where robots can come to recharge their batteries. Then, we aim to design schedules for the robots so that they can finish all their tasks while consuming as little energy and time (including the time spent for recharging) as possible. Moreover, we model the working space by a connected (possibly incomplete) graph to make the problem more practical. - Coping with environmental changes. This path planning problem takes into account not only energy limits but also changes in the physical environment, which may result in overheads (i.e., additional time and energy) that robots incur while doing their tasks. To tackle the problem from a software perspective, we first utilize Gaussian Process and Polynomial Regression to model disturbances and energy consumption, respectively, then proposed techniques to generate plans and adjust them when robots detect environmental changes. For each problem, we give a formal description, a transformation to integer (linear) programming, online algorithms, and an online algorithm. Moreover, we also rigorously analyze the proposed techniques by conducting simulations and experiments in a real network of unmanned aerial vehicles (UAVs). / Thesis / Candidate in Philosophy

Yes / The Reverse Osmosis (RO) process has been expansively used in water treatment as a result of its low energy consumption compared to thermal distillation processes, leading to reduced overall water production cost. Evaluation and minimisation of energy consumption (expressed in kWh/m3 of fresh water production) in a medium-scale spiral wound brackish water RO (BWRO) desalination plant of the Arab Potash Company (APC) are the main aims of this research. The model developed earlier by the authors has been integrated to simulate the process and achieve the main aims. Energy consumption calculations of low salinity BWRO desalination plant, with and without an energy recovery device, have been carried out using the gPROMS software suite. In other words, this research evaluated the impact of adding an energy recovery device on the RO process energy consumption of the APC, which is introduced for the first time. Also, the effects of several operating conditions of BWRO process include the feed flow rate, pressure and temperature on the performance indicators, which include the energy consumption and total plant recovery at different energy recovery device efficiencies, were studied. The simulation results showed that the total energy consumption could be reduced at low values of feed flow rates and pressures and high values of temperatures. More importantly, there is an opportunity to reduce the total energy consumption between 47% and 53.8% compared to the one calculated for the original design without an energy recovery device.

Battery Electric Vehicles and Plug-in Hybrid Vehicles are increasingly becoming more popular in recent years. Stricter regulations from government agencies to curb emissions and reduce impact on climate have led to automobile makers adopt electric powertrains. Eco-Routing is one such method to reduce energy usage in personal transport. EcoRouting is a methodology that determines the route with the least energy consumption between two points. Standard navigation systems often determine the shortest or the fastest route, emphasizing travel time. EcoRouting considers an alternative criterion - energy consumption. In this thesis, an automation methodology is presented that determines the EcoRoute among given route alternatives based on route distance, speed limits, road grades, traffic signs, driver aggression and the powertrain. There are three major objectives in this thesis: Developing the automation methodology for the determination of EcoRoute for use in on-board applications, validating the EcoRouting methodology on actual driving conditions and studying the impact of traffic on the choice of EcoRoute. The automation methodology has been developed on the Android framework for use with on-board applications on Android mobile devices. The automation methodology used to conduct sensitivity studies show that factors such as driver aggression, distance and conditional stops impact energy consumption. The comparison of results of simulation using the automation methodology against results from actual driving to validate the methodology on actual driving conditions show that transient traffic conditions can have significant impact on energy consumption. Finally, route energy consumptions for various traffic conditions are estimated using simulation to understand the impact of traffic on energy consumption and EcoRoute choice. Results that are obtained show that apart from traffic affecting the energy consumption, travel times can have an impact on choice of EcoRoute. / Master of Science / Government agencies have been introducing tighter regulations in order to improve fuel economy and reduce emissions. These regulations are targeted at reducing the impact of vehicle usage on climate. Automobile manufacturers have increasingly adopted electric powertrains to meet these regulations. Battery Electric Vehicles and Plug-in Hybrid Vehicles are more popular than ever. Other methods in reducing environmental impact by automobiles are also being conducted. EcoRouting is one such method. EcoRouting determines the route that consumes the least energy between two locations. EcoRouting requires no modifications to be done on the vehicle or its powertrain. A methodology has been developed in this thesis that takes into account various factors such as traffic signs, speed limits, road grades, powertrain and driver aggression to determine the route that consumes the least energy. Research in this thesis has been divided into three major parts: development of the automation methodology, validating the methodology for actual driving conditions and understanding the impact of traffic on energy consumption. Results of case studies show that the input parameters affect energy consumption significantly. Traveling speeds affect the energy consumption and since transient traffic conditions can affect traveling speeds, transient traffic conditions can have a significant impact on energy consumption. Since energy consumption alone is not considered in determining the EcoRoute and the travel times are also considered so as to not inconvenience the user, traffic conditions impact the choice of EcoRoute both due to differences in energy consumption and travel time.

Call number: LD2668 .T4 1985 M44 / Master of Science