Buildings account for around 40 percent of the primary energy consumption in Europe and in the United States. They also hold tremendous energy savings potential: 15 to 29 percent by 2020 for the European building stock according to a 2009 study from the European Commission. Verifying and predicting the impact of energy conservation measures in buildings is typically done through energy audits. These audits are costly, time-consuming, and may have high error margins if only limited amounts of data can be collected. The ongoing large-scale roll-out of smart meters and wireless sensor networks in buildings gives us access to unprecedented amounts of data to track energy consumption, environmental factors and building operation. This Thesis explores the possibility of using this data to verify and predict the impact of energy conservation measures, replacing energy audits with analytical software. We look at statistical analysis techniques and optimization algorithms suitable for building two regression models: one that maps environmental (e.g.: outdoor temperature) and operational factors (e.g.: opening hours) to energy consumption in a building, the other that maps building characteristics (e.g.: type of heating system) to regression coefficients obtained from the first model (which are used as energy-efficiency indicators) in a building portfolio. Following guidelines provided in the IPMVP, we then introduce methods for verifying and predicting the savings resulting from the implementation of a conservation measure in a building.

In today’s society our residents and premises consume 38 % of the total energy use in Sweden. 2011 that number corresponded to 77,8 TWh/year. Governments and the public’s strive to lower the energy use and the European Union’s goal that all member countries should lower their energy consumption with 20 % until year 2020 creates incentives to renovations and energy efficiency measures. A possible measure that could lead to reduced energy consumption for rental apartments and smaller residences like row-houses is to install individual metering. Individual metering is a method to measure how much energy is consumed in residences. Devices are installed in homes and can measure the water and heat consumption and let the residence pay for what is actually used. In this report the consumption and the costs for heat and warm water and the attitude to individual metering between two row-house areas is investigated. Both areas are located in Stockholm, in Huddinge and Farsta, where one of the areas has installed individual metering. Our study shows difference of the consumption and costs between the two areas. The installation of individual metering has result in a higher awareness for the people living in that area which has led to a reduced consumption and cost. The attitude for individual metering is positive in both areas and unchanged after the installation of individual metering. / I dagens samhälle förbrukar våra bostäder och lokaler ca 38 % av den totala energianvändningen i Sverige. 2011 motsvarade den siffran 77,8 TWh/år. Myndigheter och allmänhetens strävan att minska energiförbrukningen och EU:s målsättning att alla medlemsländer ska sänka sin energikonsumtion med 20 % fram till år 2020 skapar incitament för renoveringar och energieffektiviseringsåtgärder. En möjlig åtgärd för hyreslägenheter och mindre boenden som radhusområden är införandet av individuell mätning. Individuell mätning är en metod för att mäta energiförbrukningen i bostäder. Mätare installeras i hemmen och kan mäta vatten och värme och låter de boende betala för det de faktiskt konsumerar. I rapporten undersöks förbrukningen och kostnaderna för värme och varmvatten samt hur de boendes inställning är till individuell mätning mellan två radhusområden. Båda är belägna i Stockholm, i Huddinge respektive Farsta, där ett av områdena har infört individuell mätning. Studien visar skillnader på förbrukningen och kostnaderna mellan områdena jämfört med det andra området). Införandet av individuell mätning har medfört att de boende i området fått en högre medvetenhet vilket lett till en minskad förbrukning och kostnad. Inställningen och attityden till individuell mätning är lika positiv i båda områdena och attityden är oförändrad efter införandet i det området.

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new.

This research evaluates Intelligent Transportation System (ITS) implementation strategies to improve the safety of a freeway once a potential of a crash is detected. Among these strategies are Variable Speed Limit (VSL) and ramp metering. VSL are ITS devices that are commonly used to calm traffic in an attempt to relieve congestion and enhance throughput. With proper use, VSL can be more cost effective than adding more lanes. In addition to maximizing the capacity of a roadway, a different aspect of VSL can be realized by the potential of improving traffic safety. Through the use of multiple microscopic traffic simulations, best practices can be determined, and a final recommendation can be made. Ramp metering is a method to control the amount of traffic flow entering from on-ramps to achieve a better efficiency of the freeway. It can also have a potential benefit in improving the safety of the freeway. This thesis pursues the goal of a best-case implementation of VSL. Two loading scenarios, a fully loaded case (90% of ramp maximums) and an off-peak loading case (60% of ramp maximums), at multiple stations with multiple implementation methods are strategically attempted until a best-case implementation is found. The final recommendation for the off-peak loading is a 15 mph speed reduction for 2 miles upstream and a 15 mph increase in speed for the 2 miles downstream of the detector that shows a high crash potential. The speed change is to be implemented in 5 mph increments every 10 minutes. The recommended case is found to reduce relative crash potential from .065 to -.292, as measured by a high-speed crash prediction algorithm (Abdel-Aty et al. 2005). A possibility of crash migration to downstream and upstream locations was observed, however, the safety and efficiency benefits far outweigh the crash migration potential. No final recommendation is made for the use of VSL in the fully loaded case (low-speed case); however, ramp metering indicated a promising potential for safety improvement.

No description available.

No description available.

[EN] The electricity sector is currently undergoing a process of liberalization and separation of roles, which is being implemented under the regulatory auspices of each Member State of the European Union and, therefore, with different speeds, perspectives and objectives that must converge on a common horizon, where Europe will benefit from an interconnected energy market in which producers and consumers can participate in free competition. This process of liberalization and separation of roles involves two consequences or, viewed another way, entails a major consequence from which other immediate consequence, as a necessity, is derived. The main consequence is the increased complexity in the management and supervision of a system, the electrical, increasingly interconnected and participatory, with connection of distributed energy sources, much of them from renewable sources, at different voltage levels and with different generation capacity at any point in the network. From this situation the other consequence is derived, which is the need to communicate information between agents, reliably, safely and quickly, and that this information is analyzed in the most effective way possible, to form part of the processes of decision taking that improve the observability and controllability of a system which is increasing in complexity and number of agents involved. With the evolution of Information and Communication Technologies (ICT), and the investments both in improving existing measurement and communications infrastructure, and taking the measurement and actuation capacity to a greater number of points in medium and low voltage networks, the availability of data that informs of the state of the network is increasingly higher and more complete. All these systems are part of the so-called Smart Grids, or intelligent networks of the future, a future which is not so far. One such source of information comes from the energy consumption of customers, measured on a regular basis (every hour, half hour or quarter-hour) and sent to the Distribution System Operators from the Smart Meters making use of Advanced Metering Infrastructure (AMI). This way, there is an increasingly amount of information on the energy consumption of customers, being stored in Big Data systems. This growing source of information demands specialized techniques which can take benefit from it, extracting a useful and summarized knowledge from it. This thesis deals with the use of this information of energy consumption from Smart Meters, in particular on the application of data mining techniques to obtain temporal patterns that characterize the users of electrical energy, grouping them according to these patterns in a small number of groups or clusters, that allow evaluating how users consume energy, both during the day and during a sequence of days, allowing to assess trends and predict future scenarios. For this, the current techniques are studied and, proving that the current works do not cover this objective, clustering or dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users are developed. These techniques are tested and validated on a database of hourly energy consumption values for a sample of residential customers in Spain during years 2008 and 2009. The results allow to observe both the characterization in consumption patterns of the different types of residential energy consumers, and their evolution over time, and to assess, for example, how the regulatory changes that occurred in Spain in the electricity sector during those years influenced in the temporal patterns of energy consumption. / [ES] El sector eléctrico se halla actualmente sometido a un proceso de liberalización y separación de roles, que está siendo aplicado bajo los auspicios regulatorios de cada Estado Miembro de la Unión Europea y, por tanto, con distintas velocidades, perspectivas y objetivos que deben confluir en un horizonte común, en donde Europa se beneficiará de un mercado energético interconectado, en el cual productores y consumidores podrán participar en libre competencia. Este proceso de liberalización y separación de roles conlleva dos consecuencias o, visto de otra manera, conlleva una consecuencia principal de la cual se deriva, como necesidad, otra consecuencia inmediata. La consecuencia principal es el aumento de la complejidad en la gestión y supervisión de un sistema, el eléctrico, cada vez más interconectado y participativo, con conexión de fuentes distribuidas de energía, muchas de ellas de origen renovable, a distintos niveles de tensión y con distinta capacidad de generación, en cualquier punto de la red. De esta situación se deriva la otra consecuencia, que es la necesidad de comunicar información entre los distintos agentes, de forma fiable, segura y rápida, y que esta información sea analizada de la forma más eficaz posible, para que forme parte de los procesos de toma de decisiones que mejoran la observabilidad y controlabilidad de un sistema cada vez más complejo y con más agentes involucrados. Con el avance de las Tecnologías de Información y Comunicaciones (TIC), y las inversiones tanto en mejora de la infraestructura existente de medida y comunicaciones, como en llevar la obtención de medidas y la capacidad de actuación a un mayor número de puntos en redes de media y baja tensión, la disponibilidad de datos sobre el estado de la red es cada vez mayor y más completa. Todos estos sistemas forman parte de las llamadas Smart Grids, o redes inteligentes del futuro, un futuro ya no tan lejano. Una de estas fuentes de información proviene de los consumos energéticos de los clientes, medidos de forma periódica (cada hora, media hora o cuarto de hora) y enviados hacia las Distribuidoras desde los contadores inteligentes o Smart Meters, mediante infraestructura avanzada de medida o Advanced Metering Infrastructure (AMI). De esta forma, cada vez se tiene una mayor cantidad de información sobre los consumos energéticos de los clientes, almacenada en sistemas de Big Data. Esta cada vez mayor fuente de información demanda técnicas especializadas que sepan aprovecharla, extrayendo un conocimiento útil y resumido de la misma. La presente Tesis doctoral versa sobre el uso de esta información de consumos energéticos de los contadores inteligentes, en concreto sobre la aplicación de técnicas de minería de datos (data mining) para obtener patrones temporales que caractericen a los usuarios de energía eléctrica, agrupándolos según estos mismos patrones en un número reducido de grupos o clusters, que permiten evaluar la forma en que los usuarios consumen la energía, tanto a lo largo del día como durante una secuencia de días, permitiendo evaluar tendencias y predecir escenarios futuros. Para ello se estudian las técnicas actuales y, comprobando que los trabajos actuales no cubren este objetivo, se desarrollan técnicas de clustering o segmentación dinámica aplicadas a curvas de carga de consumo eléctrico diario de clientes domésticos. Estas técnicas se prueban y validan sobre una base de datos de consumos energéticos horarios de una muestra de clientes residenciales en España durante los años 2008 y 2009. Los resultados permiten observar tanto la caracterización en consumos de los distintos tipos de consumidores energéticos residenciales, como su evolución en el tiempo, y permiten evaluar, por ejemplo, cómo influenciaron en los patrones temporales de consumos los cambios regulatorios que se produjeron en España en el sector eléctrico durante esos años. / [CA] El sector elèctric es troba actualment sotmès a un procés de liberalització i separació de rols, que s'està aplicant davall els auspicis reguladors de cada estat membre de la Unió Europea i, per tant, amb distintes velocitats, perspectives i objectius que han de confluir en un horitzó comú, on Europa es beneficiarà d'un mercat energètic interconnectat, en el qual productors i consumidors podran participar en lliure competència. Aquest procés de liberalització i separació de rols comporta dues conseqüències o, vist d'una altra manera, comporta una conseqüència principal de la qual es deriva, com a necessitat, una altra conseqüència immediata. La conseqüència principal és l'augment de la complexitat en la gestió i supervisió d'un sistema, l'elèctric, cada vegada més interconnectat i participatiu, amb connexió de fonts distribuïdes d'energia, moltes d'aquestes d'origen renovable, a distints nivells de tensió i amb distinta capacitat de generació, en qualsevol punt de la xarxa. D'aquesta situació es deriva l'altra conseqüència, que és la necessitat de comunicar informació entre els distints agents, de forma fiable, segura i ràpida, i que aquesta informació siga analitzada de la manera més eficaç possible, perquè forme part dels processos de presa de decisions que milloren l'observabilitat i controlabilitat d'un sistema cada vegada més complex i amb més agents involucrats. Amb l'avanç de les tecnologies de la informació i les comunicacions (TIC), i les inversions, tant en la millora de la infraestructura existent de mesura i comunicacions, com en el trasllat de l'obtenció de mesures i capacitat d'actuació a un nombre més gran de punts en xarxes de mitjana i baixa tensió, la disponibilitat de dades sobre l'estat de la xarxa és cada vegada major i més completa. Tots aquests sistemes formen part de les denominades Smart Grids o xarxes intel·ligents del futur, un futur ja no tan llunyà. Una d'aquestes fonts d'informació prové dels consums energètics dels clients, mesurats de forma periòdica (cada hora, mitja hora o quart d'hora) i enviats cap a les distribuïdores des dels comptadors intel·ligents o Smart Meters, per mitjà d'infraestructura avançada de mesura o Advanced Metering Infrastructure (AMI). D'aquesta manera, cada vegada es té una major quantitat d'informació sobre els consums energètics dels clients, emmagatzemada en sistemes de Big Data. Aquesta cada vegada major font d'informació demanda tècniques especialitzades que sàpiguen aprofitar-la, extraient-ne un coneixement útil i resumit. La present tesi doctoral versa sobre l'ús d'aquesta informació de consums energètics dels comptadors intel·ligents, en concret sobre l'aplicació de tècniques de mineria de dades (data mining) per a obtenir patrons temporals que caracteritzen els usuaris d'energia elèctrica, agrupant-los segons aquests mateixos patrons en una quantitat reduïda de grups o clusters, que permeten avaluar la forma en què els usuaris consumeixen l'energia, tant al llarg del dia com durant una seqüència de dies, i que permetent avaluar tendències i predir escenaris futurs. Amb aquesta finalitat, s'estudien les tècniques actuals i, en comprovar que els treballs actuals no cobreixen aquest objectiu, es desenvolupen tècniques de clustering o segmentació dinàmica aplicades a corbes de càrrega de consum elèctric diari de clients domèstics. Aquestes tècniques es proven i validen sobre una base de dades de consums energètics horaris d'una mostra de clients residencials a Espanya durant els anys 2008 i 2009. Els resultats permeten observar tant la caracterització en consums dels distints tipus de consumidors energètics residencials, com la seua evolució en el temps, i permeten avaluar, per exemple, com van influenciar en els patrons temporals de consums els canvis reguladors que es van produir a Espanya en el sector elèctric durant aquests anys. / Benítez Sánchez, IJ. (2015). Dynamic segmentation techniques applied to load profiles of electric energy consumption from domestic users [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/59236

Hersteller mobiler Arbeitsmaschinen sind im Zuge des angestrebten CO2-Ausstiegs zunehmendem Druck ausgesetzt, die Energieeffizienz ihrer Maschinen zu verbessern. Insbesondere vor dem Hintergrund der Elektrifizierung der Antriebssysteme als Alternative zu konventionellen fossil betriebenen Verbrennungsmotoren gewinnt die Energieeffizienz wesentlich an Bedeutung. Wurden Ventilsysteme mit getrennten Steuerkanten (gSk) aufgrund der tendenziell höheren Kosten bisher oft verworfen, so lohnt sich ihr Einsatz in Zeiten teurer Energie und hoher Preise für elektrische Akkukapazität jetzt zunehmend. In den vergangenen Jahrzehnten wurde bereits sehr viel Forschungsarbeit auf dem Gebiet der gSk geleistet. Viele publizierte Ansätze zur Bewegungssteuerung, also der Steuerung von Druck und Volumenstrom, in diesen Mehrgrößensystemen zeichnen sich durch eine hohe Komplexität der Algorithmen und/oder die Notwendigkeit hoch leistungsfähiger Ventiltechnik aus. Aus der Nutzung regenerativer Betriebsmodi zur Energieeinsparung resultiert in vielen Fällen die Notwendigkeit einer Umschaltung zwischen den Modi in der laufenden Bewegung. Vergleiche der jeweils vorgeschlagenen Lösungen erfolgen meistens nur mit einer konventionellen Referenz, nicht aber untereinander, sodass dem Entwickler verborgen bleibt, welcher Lösungsansatz für die Probleme Bewegungssteuerung und Modusumschaltung in der betrachteten Anwendung am besten geeignet ist. Zudem wird bei der Entwicklung energiesparender Betriebsstrategien, also der situationsabhängigen Wahl von Solldrücken und Betriebsmodi, in der vorliegenden Literatur kaum auf die Auswirkungen der vorgeschlagenen Lösungen auf das Bedienverhalten der Maschine eingegangen. Die vorliegende Arbeit soll diese Lücken schließen. Es werden verschiedene Steuerungs- und Regelungsansätze für Druck und Volumenstrom eines gSk-Systems vorgestellt, die sich durch einfache Gleichungen und eine Parametrierung nahezu ausschließlich mit leicht verfügbaren Konstruktionsdaten, sowie Unempfindlichkeit gegenüber Parameterschwankungen im Betrieb der Maschine auszeichnen. Teilweise kommen dabei Druckwaagen oder Druckregelventile als hydraulische Regler zum Einsatz. Die Algorithmen werden mittels der linearen Systemtheorie auf Stabilität untersucht und - soweit notwendig - Prüf- und Einstellverfahren hergeleitet. Die Arbeit stellt heraus, welche nachteiligen Auswirkungen eine energieoptimale Betriebsstrategie auf das Bedienverhalten hat und schlägt als Lösung einen energetischen Kompromiss bei ungestörter Bedienbarkeit vor. Ein Wechsel zwischen verschiedenen Betriebsmodi in der laufenden Bewegung ist mit einer stetigen Umschaltung mit einem Kurzschlussventil, einer stetigen Umschaltung mit einem Rückschlagventil, sowie einer störungsfreien diskreten Umschaltung mit einer Druckwaage realisierbar. Diese drei und die aus der Literatur bekannte diskrete Umschaltung ohne weiter Maßnahmen werden miteinander verglichen. Die aus den theoretischen Vorüberlegungen favorisierten Lösungsansätze für die vorgenannten Themengebiete Bewegungssteuerung, Betriebsstrategie und Modusumschaltung werden an einem Baggerarm-Laborprüfstand mit verschiedenen Ventilsystemen in realitätsnahmen Szenarien validiert, wobei der Schwerpunkt der Versuche auf der Analyse des Bedienverhaltens und der Komponentenanforderungen liegt. Die Ergebnisse zeigen, dass es unter Beachtung und Umgehung einiger in der Arbeit identifizierter Fallstricke möglich ist, mit mobiltypischer Hardware und einfachen Algorithmen ein gut bedienbares und energieeffizientes gSk-System aufzubauen. Weiterhin bilden die erreichten Ergebnisse die Basis für anforderungsabhängige Empfehlungen bestimmter Ansätze für die jeweiligen Detailfragen Bewegungssteuerung, Modusumschaltung und Betriebsstrategie. Damit ist dem Ingenieur ein Leitfaden zur anwendungsabhängigen Entwicklung eines gSk-Ventilsystems gegeben.

Smart sensors have become and will continue to constitute an enabling technology to wirelessly connect platforms and systems and enable improved and autonomous performance. Automobiles have about two hundred sensors. Airplanes have about eight thousand sensors. With technology advancements in autonomous vehicles or fly-by-wireless, the numbers of these sensors is expected to increase significantly. The need to conserve water and energy has led to the development of advanced metering infrastructure (AMI) as a concept to support smart energy and water grid systems that would respond to emergency shut-offs or electric blackouts. Through the Internet of things (IoT) smart sensors and other network devices will be connected to enable exchange and control procedure toward reducing the operational cost and improving the efficiency of residential and commercial buildings in terms of their function or energy and water use. Powering these smart sensors with batteries or wires poses great challenges in terms of replacing the batteries and connecting the wires especially in remote and difficult-to-reach locations. Harvesting free ambient energy provides a solution to develop self-powered smart sensors that can support different platforms and systems and integrate their functionality. In this dissertation, we develop and experimentally assess the performance of harvesters that draw their energy from air or water flows. These harvesters include centimeter-scale micro wind turbines, piezo aeroelastic harvesters, and micro hydro generators. The performance of these different harvesters is determined by their capability to support wireless sensing and transmission, the level of generated power, and power density. We also develop and demonstrate the capability of multifunctional systems that can harvest energy to replenish a battery and use the harvested energy to sense speed, flow rate or temperature, and to transmit the data wirelessly to a remote location. / PHD / Smart sensors are an essential part of planned connected communities, smart cities and buildings, structural health and pollution monitoring, and autonomous systems including air and ground vehicles. For example, these sensors can be used to monitor different buildings functions such as water flow rates, pressure and temperature, smoke detectors, HVAC and fire alarms systems. Most of the current smart sensors are powered by batteries or connected to a power source with wires. Batteries will need to be replaced frequently. Wires will add a cost and weight to the system. On the other hand, energy can be harvested locally from different sources to power these sensors. In this dissertation, we develop and experimentally assess the performance of energy harvesters that draw power from air or water flows. These devices include centimeter-scale micro wind turbines, piezo aeroelastic harvesters, and micro hydro generators. The level of generated power, and power density of these devices and their capability to support wireless sensing and transmission are evaluated. We also develop and demonstrate the capability of using one device to harvest energy to replenish a battery over specified time periods and use the harvested energy and the same device to sense speed, flow rate or temperature, and to transmit the data wirelessly to a remote location over other time periods.

This thesis addressed the need for improved analysis and interpretation of primary meter half-hourly energy consumption data. The current work offers a novel benchmarking technique that was tested for 6 types of municipal buildings. This approach is different from conventional annual benchmarking mainly because it uses electricity and gas data in half-hourly periods, together with outside temperature data. A survey to European local authorities’ metering and monitoring practices was conducted in order to assess municipal energy managers' current procedures and needs in terms of data analysis to assess building energy performance and to identify potential energy saving opportunities. The benchmarking approach was developed considering the energy managers’ needs, but also the state-of the art in terms of building energy monitoring techniques, particularly building energy signatures, and the analysis techniques used on electricity grid demand forecasting. The benchmarking approach is based on the use of a metric composed of several indicators that are related to the load demand shape profile and the building energy signature. The comparison of indicators for buildings of the same type using standard scores identifies uncommon load demand profile characteristics and/or gas dependency on outside temperature in specific buildings. The metric is able to support the identification of potential energy wastage, which is linked to the detection of opportunities to save energy. The benchmarking technique was tested in 81 municipal building owned by Leicester City Council. This methodology can be applied to any non-domestic building equipped with primary meters for registering half-hourly electricity and gas consumption. In theory, this approach can also be applied to residential buildings, and to other short time series data types, for example quarter-hourly or 10 minutes interval data. The main contribution of this thesis is to improve the objectivity of building primary meter half-hourly electricity and gas consumption data analysis and interpretation by using quantitative parameters, instead of subjective visualisation techniques. The interpretation of building consumption data in short time series periods can now be streamlined, automated and perhaps incorporated in existing energy analysis software. This thesis raises questions that can lead to future research projects aiming to improve the metric and also to enlarge the scope of its application to national and European scale, to other building types and to other utilities.