Energy consumption of passenger land transport modes

This paper shows how the methodologies used in current practice might lead to an underestimation of energy consumption by different passenger transport modes, and also offers recommendations for improvements to these methodologies. The first recommendation is related to energy consumption rates. The studies reviewed use traditional energy consumption rates based on transportation demand, such as kilowatts-hour per vehicle-kilometre or kilowatts-hour per passenger-kilometre, and include other rates based on transportation supply which might prove useful. Second, energy consumption rates are dependent on factors, and the introduction of homogeneous units which are independent of these factors therefore offers a significant improvement when comparing transport modes. Third, the use of a vehicle energy consumption equation will improve the quality of the assessments. Fourth, we propose that the coefficients which define the energy consumption equation should be broken down to determine market niches and sources for improvements in energy consumption in the vehicle categories.

How much energy will your NGN consume? A model for energy consumption in next generation access networks: The case of Spain

The contribution to global energy consumption of the information and communications technology (ICT) sector has increased considerably in the last decade, along with its growing relevance to the overall economy. This trend will continue due to the seemingly ever greater use of these technologies, with broadband data traffic generated by the usage of telecommunication networks as a primary component. In fact, in response to user demand, the telecommunications industry is initiating the deployment of next generation networks (NGNs). However, energy consumption is mostly absent from the debate on these deployments, in spite of the potential impact on both expenses and sustainability. In addition, consumers are unaware of the energy impact of their choices in ultra-broadband services. This paper focuses on forecasting energy consumption in the access part of NGNs by modelling the combined effect of the deployment of two different ultra-broadband technologies (FTTH-GPON and LTE), the evolution of traffic per user, and the energy consumption in each of the networks and user devices. Conclusions are presented on the levels of energy consumption, their cost and the impact of different network design parameters. The effect of technological developments, techno-economic and policy decisions on energy consumption is highlighted. On the consumer side, practical figures and comparisons across technologies are provided. Although the paper focuses on Spain, the analysis can be extended to similar countries.

Construction cost and energy consumption resulting from energy retrofitting in an apartment building in Madrid (Spain).

This theoretical study analyzes the relation between the measures necessesary for the energy retrofitting of a residential building constructed in Madrid, their cost and the improvement of the energy rating of the dwellings. The aim of this work is to establish an evaluation methodology that allows developers and architects to obtain conclusions and orientates therm in the decisioin-making process. It will allow finding the most suitable cost-effective solutions in each case. This paper describes the methodology and the findings obtained. Energy retrofitting and the improvement of the energy behaviour of the building depend on the selection of the retrofitting solutions and also on the investment. In this case study to achieve the best energy rates it is necessary to improve the thermal performance of the envelope as well as the energy systems. Energy retrofitting means an increase in property value but it can't only be considered in economic terms. It is necessary to take into account unquantifiable aspects as increased comfort, improved sound insulation, livability, health, or the elimination of energy poverty situations.

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques.

Improved static analysis and verification of energy consumption and other resources via abstract interpretation

Resource analysis aims at inferring the cost of executing programs for any possible input, in terms of a given resource, such as the traditional execution steps, time ormemory, and, more recently energy consumption or user defined resources (e.g., number of bits sent over a socket, number of database accesses, number of calls to particular procedures, etc.). This is performed statically, i.e., without actually running the programs. Resource usage information is useful for a variety of optimization and verification applications, as well as for guiding software design. For example, programmers can use such information to choose different algorithmic solutions to a problem; program transformation systems can use cost information to choose between alternative transformations; parallelizing compilers can use cost estimates for granularity control, which tries to balance the overheads of task creation and manipulation against the benefits of parallelization. In this thesis we have significatively improved an existing prototype implementation for resource usage analysis based on abstract interpretation, addressing a number of relevant challenges and overcoming many limitations it presented. The goal of that prototype was to show the viability of casting the resource analysis as an abstract domain, and howit could overcome important limitations of the state-of-the-art resource usage analysis tools. For this purpose, it was implemented as an abstract domain in the abstract interpretation framework of the CiaoPP system, PLAI.We have improved both the design and implementation of the prototype, for eventually allowing an evolution of the tool to the industrial application level. The abstract operations of such tool heavily depend on the setting up and finding closed-form solutions of recurrence relations representing the resource usage behavior of program components and the whole program as well. While there exist many tools, such as Computer Algebra Systems (CAS) and libraries able to find closed-form solutions for some types of recurrences, none of them alone is able to handle all the types of recurrences arising during program analysis. In addition, there are some types of recurrences that cannot be solved by any existing tool. This clearly constitutes a bottleneck for this kind of resource usage analysis. Thus, one of the major challenges we have addressed in this thesis is the design and development of a novel modular framework for solving recurrence relations, able to combine and take advantage of the results of existing solvers. Additionally, we have developed and integrated into our novel solver a technique for finding upper-bound closed-form solutions of a special class of recurrence relations that arise during the analysis of programs with accumulating parameters. Finally, we have integrated the improved resource analysis into the CiaoPP general framework for resource usage verification, and specialized the framework for verifying energy consumption specifications of embedded imperative programs in a real application, showing the usefulness and practicality of the resulting tool.---ABSTRACT---El Análisis de recursos tiene como objetivo inferir el coste de la ejecución de programas para cualquier entrada posible, en términos de algún recurso determinado, como pasos de ejecución, tiempo o memoria, y, más recientemente, el consumo de energía o recursos definidos por el usuario (por ejemplo, número de bits enviados a través de un socket, el número de accesos a una base de datos, cantidad de llamadas a determinados procedimientos, etc.). Ello se realiza estáticamente, es decir, sin necesidad de ejecutar los programas. La información sobre el uso de recursos resulta muy útil para una gran variedad de aplicaciones de optimización y verificación de programas, así como para asistir en el diseño de los mismos. Por ejemplo, los programadores pueden utilizar dicha información para elegir diferentes soluciones algorítmicas a un problema; los sistemas de transformación de programas pueden utilizar la información de coste para elegir entre transformaciones alternativas; los compiladores paralelizantes pueden utilizar las estimaciones de coste para realizar control de granularidad, el cual trata de equilibrar el coste debido a la creación y gestión de tareas, con los beneficios de la paralelización. En esta tesis hemos mejorado de manera significativa la implementación de un prototipo existente para el análisis del uso de recursos basado en interpretación abstracta, abordando diversos desafíos relevantes y superando numerosas limitaciones que éste presentaba. El objetivo de dicho prototipo era mostrar la viabilidad de definir el análisis de recursos como un dominio abstracto, y cómo se podían superar las limitaciones de otras herramientas similares que constituyen el estado del arte. Para ello, se implementó como un dominio abstracto en el marco de interpretación abstracta presente en el sistema CiaoPP, PLAI. Hemos mejorado tanto el diseño como la implementación del mencionado prototipo para posibilitar su evolución hacia una herramienta utilizable en el ámbito industrial. Las operaciones abstractas de dicha herramienta dependen en gran medida de la generación, y posterior búsqueda de soluciones en forma cerrada, de relaciones recurrentes, las cuales modelizan el comportamiento, respecto al consumo de recursos, de los componentes del programa y del programa completo. Si bien existen actualmente muchas herramientas capaces de encontrar soluciones en forma cerrada para ciertos tipos de recurrencias, tales como Sistemas de Computación Algebraicos (CAS) y librerías de programación, ninguna de dichas herramientas es capaz de tratar, por sí sola, todos los tipos de recurrencias que surgen durante el análisis de recursos. Existen incluso recurrencias que no las puede resolver ninguna herramienta actual. Esto constituye claramente un cuello de botella para este tipo de análisis del uso de recursos. Por lo tanto, uno de los principales desafíos que hemos abordado en esta tesis es el diseño y desarrollo de un novedoso marco modular para la resolución de relaciones recurrentes, combinando y aprovechando los resultados de resolutores existentes. Además de ello, hemos desarrollado e integrado en nuestro nuevo resolutor una técnica para la obtención de cotas superiores en forma cerrada de una clase característica de relaciones recurrentes que surgen durante el análisis de programas lógicos con parámetros de acumulación. Finalmente, hemos integrado el nuevo análisis de recursos con el marco general para verificación de recursos de CiaoPP, y hemos instanciado dicho marco para la verificación de especificaciones sobre el consumo de energía de programas imperativas embarcados, mostrando la viabilidad y utilidad de la herramienta resultante en una aplicación real.

Cognitive strategies for reducing energy consumption in wireless sensor networks

El consumo energético de las Redes de Sensores Inalámbricas (WSNs por sus siglas en inglés) es un problema histórico que ha sido abordado desde diferentes niveles y visiones, ya que no solo afecta a la propia supervivencia de la red sino que el creciente uso de dispositivos inteligentes y el nuevo paradigma del Internet de las Cosas hace que las WSNs tengan cada vez una mayor influencia en la huella energética. Debido a la tendencia al alza en el uso de estas redes se añade un nuevo problema, la saturación espectral. Las WSNs operan habitualmente en bandas sin licencia como son las bandas Industrial, Científica y Médica (ISM por sus siglas en inglés). Estas bandas se comparten con otro tipo de redes como Wi-Fi o Bluetooth cuyo uso ha crecido exponencialmente en los últimos años. Para abordar este problema aparece el paradigma de la Radio Cognitiva (CR), una tecnología que permite el acceso oportunista al espectro. La introducción de capacidades cognitivas en las WSNs no solo permite optimizar su eficiencia espectral sino que también tiene un impacto positivo en parámetros como la calidad de servicio, la seguridad o el consumo energético. Sin embargo, por otra parte, este nuevo paradigma plantea algunos retos relacionados con el consumo energético. Concretamente, el sensado del espectro, la colaboración entre los nodos (que requiere comunicación adicional) y el cambio en los parámetros de transmisión aumentan el consumo respecto a las WSN clásicas. Teniendo en cuenta que la investigación en el campo del consumo energético ha sido ampliamente abordada puesto que se trata de una de sus principales limitaciones, asumimos que las nuevas estrategias deben surgir de las nuevas capacidades añadidas por las redes cognitivas. Por otro lado, a la hora de diseñar estrategias de optimización para CWSN hay que tener muy presentes las limitaciones de recursos de estas redes en cuanto a memoria, computación y consumo energético de los nodos. En esta tesis doctoral proponemos dos estrategias de reducción de consumo energético en CWSNs basadas en tres pilares fundamentales. El primero son las capacidades cognitivas añadidas a las WSNs que proporcionan la posibilidad de adaptar los parámetros de transmisión en función del espectro disponible. La segunda es la colaboración, como característica intrínseca de las CWSNs. Finalmente, el tercer pilar de este trabajo es teoría de juegos como algoritmo de soporte a la decisión, ampliamente utilizado en WSNs debido a su simplicidad. Como primer aporte de la tesis se presenta un análisis completo de las posibilidades introducidas por la radio cognitiva en materia de reducción de consumo para WSNs. Gracias a las conclusiones extraídas de este análisis, se han planteado las hipótesis de esta tesis relacionadas con la validez de usar capacidades cognitivas como herramienta para la reducción de consumo en CWSNs. Una vez presentada las hipótesis, pasamos a desarrollar las principales contribuciones de la tesis: las dos estrategias diseñadas para reducción de consumo basadas en teoría de juegos y CR. La primera de ellas hace uso de un juego no cooperativo que se juega mediante pares de jugadores. En la segunda estrategia, aunque el juego continúa siendo no cooperativo, se añade el concepto de colaboración. Para cada una de las estrategias se presenta el modelo del juego, el análisis formal de equilibrios y óptimos y la descripción de la estrategia completa donde se incluye la interacción entre nodos. Con el propósito de probar las estrategias mediante simulación e implementación en dispositivos reales hemos desarrollado un marco de pruebas compuesto por un simulador cognitivo y un banco de pruebas formado por nodos cognitivos capaces de comunicarse en tres bandas ISM desarrollados en el B105 Lab. Este marco de pruebas constituye otra de las aportaciones de la tesis que permitirá el avance en la investigación en el área de las CWSNs. Finalmente, se presentan y discuten los resultados derivados de la prueba de las estrategias desarrolladas. La primera estrategia proporciona ahorros de energía mayores al 65% comparados con una WSN sin capacidades cognitivas y alrededor del 25% si la comparamos con una estrategia cognitiva basada en el sensado periódico del espectro para el cambio de canal de acuerdo a un nivel de ruido fijado. Este algoritmo se comporta de forma similar independientemente del nivel de ruido siempre que éste sea espacialmente uniformemente. Esta estrategia, a pesar de su sencillez, nos asegura el comportamiento óptimo en cuanto a consumo energético debido a la utilización de teoría de juegos en la fase de diseño del comportamiento de los nodos. La estrategia colaborativa presenta mejoras respecto a la anterior en términos de protección frente al ruido en escenarios de ruido más complejos donde aporta una mejora del 50% comparada con la estrategia anterior. ABSTRACT Energy consumption in Wireless Sensor Networks (WSNs) is a known historical problem that has been addressed from different areas and on many levels. But this problem should not only be approached from the point of view of their own efficiency for survival. A major portion of communication traffic has migrated to mobile networks and systems. The increased use of smart devices and the introduction of the Internet of Things (IoT) give WSNs a great influence on the carbon footprint. Thus, optimizing the energy consumption of wireless networks could reduce their environmental impact considerably. In recent years, another problem has been added to the equation: spectrum saturation. Wireless Sensor Networks usually operate in unlicensed spectrum bands such as Industrial, Scientific, and Medical (ISM) bands shared with other networks (mainly Wi-Fi and Bluetooth). To address the efficient spectrum utilization problem, Cognitive Radio (CR) has emerged as the key technology that enables opportunistic access to the spectrum. Therefore, the introduction of cognitive capabilities to WSNs allows optimizing their spectral occupation. Cognitive Wireless Sensor Networks (CWSNs) do not only increase the reliability of communications, but they also have a positive impact on parameters such as the Quality of Service (QoS), network security, or energy consumption. These new opportunities introduced by CWSNs unveil a wide field in the energy consumption research area. However, this also implies some challenges. Specifically, the spectrum sensing stage, collaboration among devices (which requires extra communication), and changes in the transmission parameters increase the total energy consumption of the network. When designing CWSN optimization strategies, the fact that WSN nodes are very limited in terms of memory, computational power, or energy consumption has to be considered. Thus, light strategies that require a low computing capacity must be found. Since the field of energy conservation in WSNs has been widely explored, we assume that new strategies could emerge from the new opportunities presented by cognitive networks. In this PhD Thesis, we present two strategies for energy consumption reduction in CWSNs supported by three main pillars. The first pillar is that cognitive capabilities added to the WSN provide the ability to change the transmission parameters according to the spectrum. The second pillar is that the ability to collaborate is a basic characteristic of CWSNs. Finally, the third pillar for this work is the game theory as a decision-making algorithm, which has been widely used in WSNs due to its lightness and simplicity that make it valid to operate in CWSNs. For the development of these strategies, a complete analysis of the possibilities is first carried out by incorporating the cognitive abilities into the network. Once this analysis has been performed, we expose the hypotheses of this thesis related to the use of cognitive capabilities as a useful tool to reduce energy consumption in CWSNs. Once the analyses are exposed, we present the main contribution of this thesis: the two designed strategies for energy consumption reduction based on game theory and cognitive capabilities. The first one is based on a non-cooperative game played between two players in a simple and selfish way. In the second strategy, the concept of collaboration is introduced. Despite the fact that the game used is also a non-cooperative game, the decisions are taken through collaboration. For each strategy, we present the modeled game, the formal analysis of equilibrium and optimum, and the complete strategy describing the interaction between nodes. In order to test the strategies through simulation and implementation in real devices, we have developed a CWSN framework composed by a CWSN simulator based on Castalia and a testbed based on CWSN nodes able to communicate in three different ISM bands. We present and discuss the results derived by the energy optimization strategies. The first strategy brings energy improvement rates of over 65% compared to WSN without cognitive techniques. It also brings energy improvement rates of over 25% compared with sensing strategies for changing channels based on a decision threshold. We have also seen that the algorithm behaves similarly even with significant variations in the level of noise while working in a uniform noise scenario. The collaborative strategy presents improvements respecting the previous strategy in terms of noise protection when the noise scheme is more complex where this strategy shows improvement rates of over 50%.

Energy consumption in an urban wastewater treatment plant: the case of Murcia Region (Spain)

Of all the costs associated with the operation and maintenance of wastewater treatment plants (WWTPs), those associated with energy use tend to be the most significant. From this point of view, it is hence logical that energy efficiency and saving strategies should be one of the current focuses of debate amongst those involved with the management of WWTPs. The present study's objective is to determine the correlation between size and energy consumption for a WWTP. To this end, 90 WWTPs currently in service were analysed and their energetic impact quantified in terms of kWh/m3 of water treated. The results obtained demonstrate that energy consumption ratio increases as the size of WWTPs decreases, either in terms of treatment volume or population equivalent served.

Staff report on measures for reducing energy consumption for homeowners and renters.

Prepared in cooperation with the Edison Electric Institute and marketing staff of various utilities throughout the country.

Comparison of energy consumption between West Germany and the United States

"Contract no. 14-01-001-1885."

Energy consumption of environmental controls : fossil fuel, steam electric generating industry : final report /

Includes bibliography.

Energy consumption in Illinois public community colleges and universities.

Caption title.