Clustered architecture processors are preferred for embedded systems because centralized register file architectures scale poorly in terms of clock rate, chip area, and power consumption. Although clustering helps by improving clock speed, reducing energy consumption of the logic, and making the design simpler, it introduces extra overheads by way of inter-cluster communication. This communication happens over long wires having high load capacitance which leads to delay in execution and significantly high energy consumption. Inter-cluster communication also introduces many short idle cycles, therby significantly increasing the overall leakage energy consumption in the functional units. The trend towards miniatrurization of devices (and associated reduction in threshold voltage) makes energy consumption in interconnects and functional units even worse and limits the usability of clustered architectures in smaller technologies. In the past, study of leakage energy management at the architectural level has mostly focused on storage structures such as cache. Relatively, little work has been done on architecture level leakage energy management in functional units in the context of superscalar processors and energy efficient scheduling in the context of VLIW architectures. In the absence of any high level model for interconnect energy estimation, the primary focus of research in the context of interconnects has been to reduce the latency of communication and evaluation of various inter-cluster communication models. To the best of our knowledge, there has been no such work in the past from the point of view of enegy efficiency targeting clustered VLIW architectures specifically focusing on smaller technologies. Technological advancements now permit design of interconnects and functional units With varying performance and power modes. In thesis we people scheduling algorithms that aggregate the scheduling slack of instructions and communication slack of data values to exploit the low power modes of interconnects and functional units . We also propose a high level model for estimation of interconnect delay and energy (in contrast to low-level circuit level model proposed earlier) that makes it possible to carry out architectural and compiler optimizations specifically targeting the inter connect, Finally we present synergistic combination of these algorithms that simultaneously saves energy in functional units and interconnects to improve the usability of clustered architectures by archiving better overall energy-performance trade-offs. Our compiler assisted leakage energy management scheme for functional units reduces the energy consumption of functional units approximately by 15% and 17% in the context of a 2-clustered and a 4-clustered VLIW architecture respectively with negligible performance degradation over and above that offered by a hardware-only scheme. The interconnect energy optimization scheme improves the energy consumption of interconnects on an average by 41% and 46% for a 2-clustered and a 4-clustered machine respectively with 2% and 1.5% performance degradation. The combined scheme options slightly better energy benefit in functional units and 37% and 43% energy benefit in interconnect with slightly higher performance degradation. Even with the conservative estimates of contribution of functional unit interconnect to overall processor energy consumption the proposed combined scheme obtains on an average 8% and 10% improvement in overall energy delay product with 3.5% and 2% performance degradation for a 2-clustered and a 4-clustered machine respectively. We present a detailed experimental evaluation of the proposed schemes using the Trimaran compiler infrastructure.

Emerging low power, embedded, wireless sensor devices are useful for wide range of applications, yet have very limited processing storage and especially energy resources. Sensor networks have a wide variety of applications in medical monitoring, environmental sensing and military surveillance. Due to the large number of sensor nodes that may be deployed and the required long system lifetimes, replacing the battery is not an option. Sensor systems must utilize the minimal possible energy while operating over a wide range of operating scenarios. The most of the efforts in the energy management in sensor networks have concentrated on minimizing energy consumption in the communication subsystem. Some researchers have also dealt with the issue of minimizing the energy in computing subsystem of a sensor network node. Some proposals using energy aware software have also been made. Relatively little work has been done on compiler controlled energy management in sensor networks. In this thesis, we present our investigations on how compiler techniques can be used to minimize CPU energy consumption in sensor network nodes. One effectively used energy management technique in general purpose processors, is dynamic voltage scaling. In this thesis we implement and evaluate a compiler assisted DVS algorithm and show its usefulness for a small sensor node processor. We were able to achieve an energy saving of 29% with a little performance slowdown. Scratchpad memories have been widely used for improving performance. In this thesis we show that if the scratchpad size for the system is chosen carefully, then large energy savings can be achieved by using a compiler assisted scratchpad allocation policy. With a small size of 512 byte scratchpad memory we were able to achieve 50% of energy savings. We also studied the behavior of dynamic voltage scaling in presence of scratchpad memory. Our results show that in presence of scratchpad memory less opportunities are found for applying dynamic voltage scaling techniques. The sensor network community lacks a comprehensive benchmark suite, for our study we also implemented a set of applications, representative of computational workload on sensor network nodes. The techniques studied in this thesis can easily be integrated with existing energy management techniques in sensor networks, yielding in additional energy savings.

In computer security, cryptographic algorithms and protocols are required to ensure security of data and applications. This research investigates techniques to reduce the energy consumed by cryptographic hash functions. The specific hash functions considered are Message Digest-2 (MD2), Message Digest-5 (MD5), Secure Hash Algorithm-1 (SHA-1) and Secure Hash Algorithm-2 (SHA-2). The discussion around energy conservation in handheld devices like laptops and mobile devices is gaining momentum. Research has been done at the hardware and operating system levels to reduce the energy consumed by these devices. However, research on conserving energy at the application level is a new approach. This research is motivated by the energy consumed by anti-virus applications which use computationally intensive hash functions to ensure security. To reduce energy consumption by existing hash algorithms, the generic energy complexity model, designed by Roy et al. [Roy13], has been applied and tested. This model works by logically mapping the input across the eight available memory banks in the DDR3 architecture and accessing the data in parallel. In order to reduce the energy consumed, the data access pattern of the hash functions has been studied and the energy complexity model has been applied to hash functions to redesign the existing algorithms. These experiments have shown a reduction in the total energy consumed by hash functions with different degrees of parallelism of the input message, as the energy model predicted, thereby supporting the applicability of the energy model on the different hash functions chosen for the study. The study also compared the energy consumption by the hash functions to identify the hash function suitable for use based on required security level. Finally, statistical analysis was performed to verify the difference in energy consumption between MD5 and SHA2.

Les piles à combustibles sont considérées comme une énergie d’avenir, notamment grâce à leur caractère non polluant à l’usage. Cependant, le déploiement de ces solutions à grande échelle est encore conditionné par l’amélioration de leurs performances et surtout de leur durabilité afin de garantir une industrialisation à faible coût. L’application de la pile à combustible au domaine des transports impose en plus un fonctionnement à puissance variable, ce qui complique l’amélioration des performances et de la durabilité. L’approche retenue pour ces travaux consiste en la conception d’une loi de gestion du système qui génère les conditions opératoires optimales à appliquer au stack (pressions, température, courant, stoechiométries) en fonction de la demande en puissance, de l’état de santé de la pile (perte de surface active) et du taux d’humidité actuel. L’optimalité est entendue au sens de l’augmentation du rendement système et de la diminution des dégradations du platine et de la membrane. Cette loi se base sur des modèles de dégradations et de performances d’un système pile à combustible. Cette loi de gestion requiert pour fonctionner les données de l’état de santé de la pile et du taux d’humidité. L’évaluation de l’état de santé de la pile fait déjà l’objet de nombreux travaux de diagnostic. En revanche, le taux d’humidité doit être estimé par un observateur d’état car les capteurs d’humidité ne sont pas fiables pour une application transport. Pour cela, un observateur d’état a été développé pour estimer les humidités relatives dans les canaux du stack et aussi le chargement en eau de la membrane, la quantité d’hydrogène à l’anode ainsi que la saturation d’azote à l’anode. Cette dernière donnée permet de proposer une stratégie de purge pour une architecture dead-end basée sur la saturation d’azote, qui limite les pertes en hydrogène et réduit les dégradations liées à cette architecture. / Fuel cells are considered as a promising source of energy for the future, thanks to their non-polluting aspect. However, the deployment of these solutions on a large scale is still conditioned by the improvement of their performance and especially of their durability in order to guarantee a low cost industrialization. The transport application also imposes a variable power demand, which complicates the improvement of performance and durability. The approach adopted for this work consists of the design of a system management law that generates the optimal operating conditions to be applied to the stack (pressures, temperature, current, stoichiometries) as a function of the power demand, the state of health (active surface loss) and current humidity. Optimality is understood in the sense of increasing system efficiency and decreasing the degradation of the membrane and the platinum dissolution. This law is based on degradation and performance models of a fuel cell system. This management law requires in real time the data of the state of health of the fuel cell and the humidity rate. The assessment of the state of health is already the subject of many diagnostic work. On the other hand, the humidity rate must be estimated by a state observer because the humidity sensors are not reliable for a transport application. Therefore, a state observer was developed to estimate the relative humidities in the stack channels and also the membrane water content, the hydrogen at the anode as well as the nitrogen saturation at the anode. This last data makes it possible to propose a purge strategy for a dead-end architecture, based on nitrogen saturation, which limits the losses in hydrogen and reduces the damage associated with this architecture.

The thesis deals with the environmental assessment of buildings, concrete objects Letní lázně at Karlova Studánka. The first part describes the issue and legislation. There is also described a method of experimental measurement and methods of moisture measurement. The second part deals with the analysis of the specified object, description of selected renewable sources and description of possible modifications of the object. The third part is devoted to modeling and simulation of the specified object in DesignBuilder. Using the model were created various simulations with various modifications in order to reduce energy intensity of the object. Results were then evaluated and compared.

This thesis deals with electric train optimal control problem with a focus on time and energy optimization. The problem is extended by considering specific speed contraints. We are able to design the sequence of control settings by the use of the optimal control theory. Optimization tools in Matlab environment are used to determine the numerical solution of the considered task.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

Contain of master´s thesis is a energy assessment of multifunctional building in Komňátka. The theoretical part deals with the analysis of legislativ documents, current technical solution and practical application to the specified building. In the practical part thesis focus on experimental determination of the heat transfer coefficient using thermography.

The main aim of this thesis is optimization of energy demands in a new administrative high-rise building Vienna Point II located in Brno. Experimental part of the thesis deals with determination of real energy consumption during winter season and preparation of a set of climate data for theoretical simulations. Theoretical part is based on data obtained by experimental methods and is aimed on optimization of energy demands. The effect of suggested solution is analysed by software solutions which simulate building operation. Resulting energy demands obtained by simulations are compared with real energy consumption.

This research focuses on developing strategies for the optimal control of large-scale Combined Cooling, Heating and Power (CCHP) systems to meet electricity, heating, and cooling demands, and evaluating the cost savings potential associated with it. Optimal control of CCHP systems involves the determination of the mode of operation and set points to satisfy the specific energy requirements for each time period. It is very complex to effectively design optimal control strategies because of the stochastic behavior of energy loads and fuel prices, varying component designs and operational limitations, startup and shutdown events and many more. Also, for large-scale systems, the problem involves a large number of decision variables, both discrete and continuous, and numerous constraints along with the nonlinear performance characteristic curves of equipment. In general, the CCHP energy dispatch problem is intrinsically difficult to solve because of the non-convex, non-differentiable, multimodal and discontinuous nature of the optimization problem along with strong coupling to multiple energy components. <div><br></div><div>This work presents a solution methodology for optimizing the operation of a campus CCHP system using a detailed network energy flow model solved by a hybrid approach combining mixed-integer linear programming (MILP) and nonlinear programming (NLP) optimization techniques. In the first step, MILP optimization is applied to a plant model that includes linear models for all components and a penalty for turning on or off the boilers and steam chillers. The MILP step determines which components need to be turned on and their respective load needed to meet the campus energy demand for the chosen time period (short, medium or long term) with one-hour resolution. Based on the solution from MILP solver as a starting point, the NLP optimization determines the actual hourly state of operation of selected components based on their nonlinear performance characteristics. The optimal energy dispatch algorithm provides operational signals associated with resource allocation ensuring that the systems meet campus electricity, heating, and cooling demands. The chief benefits of this formulation are its ability to determine the optimal mix of equipment with on/off capabilities and penalties for startup and shutdown, consideration of cost from all auxiliary equipment and its applicability to large-scale energy systems with multiple heating, cooling and power generation units resulting in improved performance. </div><div><br></div><div>The case-study considered in this research work is the Wade Power Plant and the Northwest Chiller Plant (NWCP) located at the main campus of Purdue University in West Lafayette, Indiana, USA. The electricity, steam, and chilled water are produced through a CCHP system to meet the campus electricity, heating and cooling demands. The hybrid approach is validated with the plant measurements and then used with the assumption of perfect load forecasts to evaluate the economic benefits of optimal control subjected to different operational conditions and fuel prices. Example cost optimizations were performed for a 24-hour period with known cooling, heating, and electricity demand of Purdue’s main campus, and based on actual real-time prices (RTP) for purchasing electricity from utility. Three optimization cases were considered for analysis: MILP [no on/off switch penalty (SP)]; MILP [including on/off switch penalty (SP)] and NLP optimization. Around 3.5% cost savings is achievable with both MILP optimization cases while almost 10.7% cost savings is achieved using the hybrid MILP-NLP approach compared to the current plant operation. For the selected components from MILP optimization, NLP balances the equipment performance to operate at the state point where its efficiency is maximum while still meeting the demand. Using this hybrid approach, a high-quality global solution is determined when the linear model is feasible while still taking into account the nonlinear nature of the problem. </div><div><br></div><div>Simulations were extended for different seasons to examine the sensitivity of the optimization results to differences in electric, heating and cooling demand. All the optimization results suggest there are opportunities for potential cost savings across all seasons compared to the current operation of the power plant. For a large CCHP plant, this could mean significant savings for a year. The impact of choosing different time range is studied for MILP optimization because any changes in MILP outputs impact the solutions of NLP optimization. Sensitivity analysis of the optimized results to the cost of purchased electricity and natural gas were performed to illustrate the operational switch between steam and electric driven components, generation and purchasing of electricity, and usage of coal and natural gas boilers that occurs for optimal operation. Finally, a modular, generalizable, easy-to-configure optimization framework for the cost-optimal control of large-scale combined cooling, heating and power systems is developed and evaluated.</div>