TY - JOUR A1 - Singhal, Puja A1 - Pahle, Michael A1 - Kalkuhl, Matthias A1 - Sommer, Stephan A1 - Levesque, Antoine A1 - Berneiser, Jessica T1 - Beyond good faith BT - why evidence-based policy is necessary to decarbonize buildings cost-effectively JF - SSRN eLibrary / Social Science Research Network N2 - The ambitious climate targets set by industrialized nations worldwide cannot be met without decarbonizing the building stock. Using Germany as a case study, this paper takes stock of the extensive set of energy efficiency policies that are already in place and clarifies that they have been designed “in good faith” but lack in overall effectiveness as well as cost-efficiency in achieving these climate targets. We map out the market failures and behavioural considerations that are potential reasons for why realized energy savings fall below expectations and why the household adoption of energy-efficient and low-carbon technologies has remained low. We highlight the pressing need for data and modern empirical research to develop targeted and cost-effective policies seeking to correct these market failures. To this end, we identify some key research questions and identify gaps in the data required for evidence-based policy. KW - energy efficiency KW - decarbonization KW - housing sector KW - heat demand KW - evidence-based policy Y1 - 2021 U6 - https://doi.org/10.2139/ssrn.3947800 SN - 1556-5068 PB - SSRN - Elsevier CY - Rochester, NY ER - TY - JOUR A1 - Ollier, Lana A1 - Melliger, Marc André A1 - Lilliestam, Johan T1 - Friends or foes? BT - Political synergy or competition between renewable energy and energy efficiency policy JF - Energies : open-access journal of related scientific research, technology development and studies in policy and management N2 - Energy efficiency measures and the deployment of renewable energy are commonly presented as two sides of the same coin-as necessary and synergistic measures to decarbonize energy systems and reach the temperature goals of the Paris Agreement. Here, we quantitatively investigate the policies and performances of the EU Member States to see whether renewables and energy efficiency policies are politically synergistic or if they rather compete for political attention and resources. We find that Member States, especially the ones perceived as climate leaders, tend to prioritize renewables over energy efficiency in target setting. Further, almost every country performs well in either renewable energy or energy efficiency, but rarely performs well in both. We find no support for the assertion that the policies are synergistic, but some evidence that they compete. However, multi-linear regression models for performance show that performance, especially in energy efficiency, is also strongly associated with general economic growth cycles, and not only efficiency policy as such. We conclude that renewable energy and energy efficiency are not synergistic policies, and that there is some competition between them. KW - energy efficiency KW - renewable energy KW - climate policy KW - policy cycle KW - EU KW - policy competition Y1 - 2020 U6 - https://doi.org/10.3390/en13236339 SN - 1996-1073 VL - 13 IS - 23 PB - MDPI CY - Basel ER - TY - THES A1 - Arodudu, Oludunsin Tunrayo T1 - Sustainability assessment of agro-bioenergy systems using energy efficiency indicators BT - energy efficiency assessment of agro-bioenergy systems N2 - The sustainability of agro-bioenergy systems is dependent on many factors, some local or regional in implementation, some others global in nature. This study assessed the effects of often ignored local and regional factors (e.g. alternative agronomic factor options, alternative agricultural production systems, alternative biomass flows, alternative conversion technologies etc. The results from this study suggests that key to enhancing the energy efficiency (and by extension the sustainability) of agro-bioenergy systems is paying attention to local and regional factors such as biomass conversion technology, alternative agronomic factor options, alternative agricultural production systems and available biomass flows. KW - biomass KW - bioenergy KW - energy efficiency KW - sustainability KW - local and regional factors KW - agronomic factors KW - agricultural production systems KW - biomass flows Y1 - 2017 ER - TY - GEN A1 - Arodudu, Oludunsin Tunrayo A1 - Helming, Katharina A1 - Wiggering, Hubert A1 - Voinov, Alexey T1 - Bioenergy from low-intensity agricultural systems BT - an energy efficiency analysis N2 - In light of possible future restrictions on the use of fossil fuel, due to climate change obligations and continuous depletion of global fossil fuel reserves, the search for alternative renewable energy sources is expected to be an issue of great concern for policy stakeholders. This study assessed the feasibility of bioenergy production under relatively low-intensity conservative, eco-agricultural settings (as opposed to those produced under high-intensity, fossil fuel based industrialized agriculture). Estimates of the net energy gain (NEG) and the energy return on energy invested (EROEI) obtained from a life cycle inventory of the energy inputs and outputs involved reveal that the energy efficiency of bioenergy produced in low-intensity eco-agricultural systems could be as much as much as 448.5–488.3 GJ·ha−1 of NEG and an EROEI of 5.4–5.9 for maize ethanol production systems, and as much as 155.0–283.9 GJ·ha−1 of NEG and an EROEI of 14.7–22.4 for maize biogas production systems. This is substantially higher than for industrialized agriculture with a NEG of 2.8–52.5 GJ·ha−1 and an EROEI of 1.2–1.7 for maize ethanol production systems, as well as a NEG of 59.3–188.7 GJ·ha−1 and an EROEI of 2.2–10.2 for maize biogas production systems. Bioenergy produced in low-intensity eco-agricultural systems could therefore be an important source of energy with immense net benefits for local and regional end-users, provided a more efficient use of the co-products is ensured. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 351 KW - bioenergy KW - biofuel KW - energy efficiency KW - NEG KW - EROEI KW - high-intensity industrialized agricultural production systems KW - low-intensity eco-agricultural production systems Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-400403 ER - TY - THES A1 - Takouna, Ibrahim T1 - Energy-efficient and performance-aware virtual machine management for cloud data centers T1 - Energieeffizientes und performancebewusstes Management virtueller Maschinen für Cloud Datenzentren N2 - Virtualisierte Cloud Datenzentren stellen nach Bedarf Ressourcen zur Verfügu-ng, ermöglichen agile Ressourcenbereitstellung und beherbergen heterogene Applikationen mit verschiedenen Anforderungen an Ressourcen. Solche Datenzentren verbrauchen enorme Mengen an Energie, was die Erhöhung der Betriebskosten, der Wärme innerhalb der Zentren und des Kohlendioxidausstoßes verursacht. Der Anstieg des Energieverbrauches kann durch ein ineffektives Ressourcenmanagement, das die ineffiziente Ressourcenausnutzung verursacht, entstehen. Die vorliegende Dissertation stellt detaillierte Modelle und neue Verfahren für virtualisiertes Ressourcenmanagement in Cloud Datenzentren vor. Die vorgestellten Verfahren ziehen das Service-Level-Agreement (SLA) und die Heterogenität der Auslastung bezüglich des Bedarfs an Speicherzugriffen und Kommunikationsmustern von Web- und HPC- (High Performance Computing) Applikationen in Betracht. Um die präsentierten Techniken zu evaluieren, verwenden wir Simulationen und echte Protokollierung der Auslastungen von Web- und HPC- Applikationen. Außerdem vergleichen wir unser Techniken und Verfahren mit anderen aktuellen Verfahren durch die Anwendung von verschiedenen Performance Metriken. Die Hauptbeiträge dieser Dissertation sind Folgendes: Ein Proaktives auf robuster Optimierung basierendes Ressourcenbereitstellungsverfahren. Dieses Verfahren erhöht die Fähigkeit der Hostes zur Verfüg-ungsstellung von mehr VMs. Gleichzeitig aber wird der unnötige Energieverbrauch minimiert. Zusätzlich mindert diese Technik unerwünschte Ände-rungen im Energiezustand des Servers. Die vorgestellte Technik nutzt einen auf Intervall basierenden Vorhersagealgorithmus zur Implementierung einer robusten Optimierung. Dabei werden unsichere Anforderungen in Betracht gezogen. Ein adaptives und auf Intervall basierendes Verfahren zur Vorhersage des Arbeitsaufkommens mit hohen, in kürzer Zeit auftretenden Schwankungen. Die Intervall basierende Vorhersage ist implementiert in der Standard Abweichung Variante und in der Median absoluter Abweichung Variante. Die Intervall-Änderungen basieren auf einem adaptiven Vertrauensfenster um die Schwankungen des Arbeitsaufkommens zu bewältigen. Eine robuste VM Zusammenlegung für ein effizientes Energie und Performance Management. Dies ermöglicht die gegenseitige Abhängigkeit zwischen der Energie und der Performance zu minimieren. Unser Verfahren reduziert die Anzahl der VM-Migrationen im Vergleich mit den neu vor kurzem vorgestellten Verfahren. Dies trägt auch zur Reduzierung des durch das Netzwerk verursachten Energieverbrauches. Außerdem reduziert dieses Verfahren SLA-Verletzungen und die Anzahl von Änderungen an Energiezus-tänden. Ein generisches Modell für das Netzwerk eines Datenzentrums um die verzö-gerte Kommunikation und ihre Auswirkung auf die VM Performance und auf die Netzwerkenergie zu simulieren. Außerdem wird ein generisches Modell für ein Memory-Bus des Servers vorgestellt. Dieses Modell beinhaltet auch Modelle für die Latenzzeit und den Energieverbrauch für verschiedene Memory Frequenzen. Dies erlaubt eine Simulation der Memory Verzögerung und ihre Auswirkung auf die VM-Performance und auf den Memory Energieverbrauch. Kommunikation bewusste und Energie effiziente Zusammenlegung für parallele Applikationen um die dynamische Entdeckung von Kommunikationsmustern und das Umplanen von VMs zu ermöglichen. Das Umplanen von VMs benutzt eine auf den entdeckten Kommunikationsmustern basierende Migration. Eine neue Technik zur Entdeckung von dynamischen Mustern ist implementiert. Sie basiert auf der Signal Verarbeitung des Netzwerks von VMs, anstatt die Informationen des virtuellen Umstellung der Hosts oder der Initiierung der VMs zu nutzen. Das Ergebnis zeigt, dass unsere Methode die durchschnittliche Anwendung des Netzwerks reduziert und aufgrund der Reduzierung der aktiven Umstellungen Energie gespart. Außerdem bietet sie eine bessere VM Performance im Vergleich zu der CPU-basierten Platzierung. Memory bewusste VM Zusammenlegung für unabhängige VMs. Sie nutzt die Vielfalt des VMs Memory Zuganges um die Anwendung vom Memory-Bus der Hosts zu balancieren. Die vorgestellte Technik, Memory-Bus Load Balancing (MLB), verteilt die VMs reaktiv neu im Bezug auf ihre Anwendung vom Memory-Bus. Sie nutzt die VM Migration um die Performance des gesamtem Systems zu verbessern. Außerdem sind die dynamische Spannung, die Frequenz Skalierung des Memory und die MLB Methode kombiniert um ein besseres Energiesparen zu leisten. N2 - Virtualized cloud data centers provide on-demand resources, enable agile resource provisioning, and host heterogeneous applications with different resource requirements. These data centers consume enormous amounts of energy, increasing operational expenses, inducing high thermal inside data centers, and raising carbon dioxide emissions. The increase in energy consumption can result from ineffective resource management that causes inefficient resource utilization. This dissertation presents detailed models and novel techniques and algorithms for virtual resource management in cloud data centers. The proposed techniques take into account Service Level Agreements (SLAs) and workload heterogeneity in terms of memory access demand and communication patterns of web applications and High Performance Computing (HPC) applications. To evaluate our proposed techniques, we use simulation and real workload traces of web applications and HPC applications and compare our techniques against the other recently proposed techniques using several performance metrics. The major contributions of this dissertation are the following: proactive resource provisioning technique based on robust optimization to increase the hosts' availability for hosting new VMs while minimizing the idle energy consumption. Additionally, this technique mitigates undesirable changes in the power state of the hosts by which the hosts' reliability can be enhanced in avoiding failure during a power state change. The proposed technique exploits the range-based prediction algorithm for implementing robust optimization, taking into consideration the uncertainty of demand. An adaptive range-based prediction for predicting workload with high fluctuations in the short-term. The range prediction is implemented in two ways: standard deviation and median absolute deviation. The range is changed based on an adaptive confidence window to cope with the workload fluctuations. A robust VM consolidation for efficient energy and performance management to achieve equilibrium between energy and performance trade-offs. Our technique reduces the number of VM migrations compared to recently proposed techniques. This also contributes to a reduction in energy consumption by the network infrastructure. Additionally, our technique reduces SLA violations and the number of power state changes. A generic model for the network of a data center to simulate the communication delay and its impact on VM performance, as well as network energy consumption. In addition, a generic model for a memory-bus of a server, including latency and energy consumption models for different memory frequencies. This allows simulating the memory delay and its influence on VM performance, as well as memory energy consumption. Communication-aware and energy-efficient consolidation for parallel applications to enable the dynamic discovery of communication patterns and reschedule VMs using migration based on the determined communication patterns. A novel dynamic pattern discovery technique is implemented, based on signal processing of network utilization of VMs instead of using the information from the hosts' virtual switches or initiation from VMs. The result shows that our proposed approach reduces the network's average utilization, achieves energy savings due to reducing the number of active switches, and provides better VM performance compared to CPU-based placement. Memory-aware VM consolidation for independent VMs, which exploits the diversity of VMs' memory access to balance memory-bus utilization of hosts. The proposed technique, Memory-bus Load Balancing (MLB), reactively redistributes VMs according to their utilization of a memory-bus using VM migration to improve the performance of the overall system. Furthermore, Dynamic Voltage and Frequency Scaling (DVFS) of the memory and the proposed MLB technique are combined to achieve better energy savings. KW - Energieeffizienz KW - Cloud Datenzentren KW - Ressourcenmanagement KW - dynamische Umsortierung KW - energy efficiency KW - cloud datacenter KW - resource management KW - dynamic consolidation Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-72399 ER -