Browsing by Subject "energy efficiency"
Now showing items 1-8 of 8
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(2024)Buildings consume approximately 40% of global energy, hence, understanding and analyzing energy consumption patterns of buildings is essential in bringing desirable insights to building management stakeholders for better decision-making and energy efficiency. Based on a specific use case of a Finnish building management company, this thesis presents the challenge of optimizing energy consumption forecasting and building management by addressing the shortcomings of current individual building-level forecasting approaches and the dynamic nature of building energy use. The research investigates the plausibility of a system of building clusters by studying the representative cluster profiles and dynamic cluster changes. We focus on a dataset comprising hourly energy consumption time series from a variety of Finnish university buildings, employing these as subjects to implement a novel stream clustering approach called ClipStream. ClipStream is an attibute-based stream clustering algorithm to perform continuous online clustering of time series data batches that involves iterative data abstraction, clustering, and change detection phases. This thesis shows that it was plausible to build clusters of buildings based on energy consumption time series. 23 buildings were successfully clustered into 3-5 clusters during each two-week window of the period of investigation. The study’s findings revealed distinct and evolving energy consumption clusters of buildings and characterized 7 predominant cluster profiles, which reflected significant seasonal variations and operational changes over time. Qualitative analyses of the clusters primarily confirmed the noticeable shifts in energy consumption patterns from 2019 to 2022, underscoring the potential of our approach to enhance forecasting efficiency and management effectiveness. These findings could be further extended to establish energy policy, building management practices, and broader sustainability efforts. This suggests that improved energy efficiency can be achieved through the application of machine learning techniques such as cluster analysis.
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(2010)In Finland energy requirement is high because of the cold climate and long distances. Energy is needed to ensure the welfare and an industrial life’s needs. Energy taxation, emissions trading and subventions are well known political instruments for decreasing energy consumption. Energy efficiency has become a famous policy for reducing energy consumption without lowering the quality of energy services. There have always been critics for the energy efficiency and its consequences among the researchers. It is possible to have improving energy efficiency, while still seeing rises in energy consumption. This phenomenon is known as a rebound effect. If energy consumption rises above the level it would have been without efficiency improvements the phenomena is called backfire effect. The objective of this thesis was to find out how to investigate the rebound-effect, what are the critical factors of it and present the results of the resent empirical studies. Attention has been also in finding out what energy efficiency is, how it appears in economical models and why it is an important research area. The effects of energy efficiency are difficult to allocate between different economic factors. Hence it seems that a computable general equilibrium framework is obvious tool to investigate the rebound effect in the national economy level though it’s quite complicated. The production function and the elasticity of substitution seem to be crucial for the size of the rebound effect in a computable general equilibrium framework. It was observed that when the elasticity of substitution was high, the rebound effect was also high. For this reason the form of production function is crucial, it should be one where the elasticity of substitution is not a constant. Empirical studies have established evidence of the rebound effect but its size varies with different areas, with a different elasticity of substitution and in different time periods. In some scenarios even backfire was observed. None of the cases reach total utility of the efficiency improvements.
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Energy Efficiency of Bluetooth Low Energy and IEEE 802.15.4 in Constrained Wireless Sensor Networks (2020)The Internet of Things (IoT) paradigm is seeing rapid adoption across multiple domains—industry, enterprise, agriculture, smart cities, households, only to name a few. IoT applications often require wireless autonomy, thereby placing challenging requirements on communication techniques and power supply methods. Wireless networking using devices with constrained energy, as often is the case in wireless sensor networks (WSN), provokes explicit considerations around the conservation of the supplied power on the one hand and the efficiency of the power drawn and energy used on the other. As radio communications characteristically consume the bulk of all energy in wireless IoT systems, this constrained energy budget combined with aspirations for terminal device lifetime sets requirements for the communications protocols and techniques used. This thesis examines two open architecture low-power wide-area network (LPWAN) standards with mesh networking support, along with their energy consumption profile in the context of power-constrained wireless sensor networks. The introductory section is followed by an overview of IoT and WSN foundations and technologies. The following section describes the IEEE 802.15.4 standard and ecosystem, followed by the Bluetooth LE and Bluetooth Mesh standards. A discussion on these standards' characteristics, behavior, and applicability to power-constrained sensor networks is presented.
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(2012)The aim of this study was to compare warm white LED and High Pressure Sodium (HPS) -lamps in the greenhouse cultivation of lettuce. Two experiments were carried out in which lettuce growth and external quality was observed, the effect of lamps on leaf temperature was measured and the electricity consumption of LED- and HPS-lamps was compared. First experiment carried out with iceberg lettuce (´Frillice`) and second experiment with red oakleaf lettuce (´Rouxai´). In the second experiment, the effect of light treatment on the color of leaves was also investigated. The presence of tipburn was another measurement of external quality in both experiments. LED-lamp with DLC-sensor (Dynamic Light Control), which was designed to optimize the illumination according to the existing natural light, was also included in the experiments. Light quality or observed differences in temperatures or relative humidity did not significantly affect the fresh weight or external quality of ice berg lettuce. Oakleaf lettuces grown under LED-light were much smaller and they had more tipburn symptoms compared to HPS-treatment. No significant differences were found in the anthocyaninlevels of oakleaf lettuce grown under different lightning treatments. LED -lighting consumed about 22% less electricity than HPS-lamp in both experiments. However, energy efficiency of HPS- and LED-lamps cannot be directly compared, since HPS-lamps illuminated larger area than the LED- luminaires. DLCsensor was able to adjust illumination according to natural light and to reduce energy consumption, but it did not increase fresh weight accumulation in relation to power consumption compared to LED-luminaire without DLC.
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(2020)The reduction of greenhouse gas emissions is one of the EU's top priorities for climate goals as it is for Helsinki. Emissions from heating alone stand for over half of the total emissions in Helsinki, presenting smart heating as an excellent opportunity to reduce both energy consumption and greenhouse gas emissions. Smart heating has gained attention as a means for reducing energy consumption due to its increased energy efficiency and automatic function. Previous studies confirm users having a more significant impact on residential heating consumption than previously considered. However, there is less understanding of what factors influences the user while using smart heating and how smart heating impacts the user. This study aims to contribute to better understand the different influencing factors by focusing on heating behavior and user experience with smart heating thermostats in a residential apartment building in Lauttasaari, Helsinki. A modified version of the Unified Theory of Acceptance of Technology (UTAUT) is used as a base. The model uses the original categories along with two added categories. Instead of using the traditional questionnaire as a method, this research uses semi-structured interviews to get a deeper perspective on the experiences in the post-implementation stage. The results indicate the most evident user experience influences to be information, trust, and the use of itself. Each user's life situation has an impact on the indoor temperature and the heating schedule. The smart thermostat increases control over indoor temperatures and individual heating possibilities due to more setting options. By gaining more control, the smart thermostat enabled the user to disregard the heating by letting the smart thermostat work in the background. Even with an increase of control, some of the participants experienced difficulties using the mobile application. Since this study is limited due to short follow-up time and small sample size, more comprehensive and in-depth research is required for the results to apply to a general population. This study shows a new point of view for influences towards the use of smart thermostats and brings up the potential benefits it can have for the city of Helsinki.
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(2022)Energy subsidies are increasingly used by governments to encourage individuals to improve the energy efficiency of their homes with energy renovations. However, the existing literature on these subsidy programmes has raised concerns that many of the subsidy recipients might have undertaken energy renovations even without the subsidy. If energy subsidies have no effect on the decision to undertake an energy renovation, or the scale or timing of the renovation, this raises serious questions about their cost-effectiveness. In this thesis, the effects of the Finnish energy subsidy programme, launched in 2020, on renovation choices were assessed using a bunching methodology. The subsidy paid to an individual is proportional to their renovation spending up to certain maximum thresholds. As a result of these thresholds, some individuals whose subsidised renovation spending would otherwise have just exceeded the maximum, now have an incentive to locate almost exactly at this maximum value. Using a bunching design to check for excess mass in the distribution of subsidised renovation spending around the thresholds, it was possible to evaluate whether individuals are responding to this incentive. Further analysis on bunching was done by evaluating the determinants of bunching with a probit model, and exploring how bunching relates to applicants’ survey responses using multiple correspondence analysis. The results from the bunching analysis demonstrate that there is significant excess mass in the distribution around subsidy thresholds, implying that some individuals are responding to the incentives created by the subsidy. Further analysis on bunching, however, highlights that the result is local and should not be generalised to applicants further from the maximum thresholds. Although the results suggest that those undertaking a more extensive renovation may have had a slightly larger behavioural response, further analysis was not able to distinguish the determinants of bunching. The behavioural response identified by bunching indicates that the subsidies are having an effect on some individuals’ choices concerning the scale and quality of their energy renovation. It should, however, be noted that this is not a causal effect and cannot be generalised to other energy subsidy programmes.
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(2023)Energy usage and efficiency is an important topic in the area of cloud computing. It is estimated that around 10% of the world’s energy consumption goes towards the global ICT system [1]. One key aspect of the cloud is virtualization, which allows for the isolation and distribution of system resources through the use of virtual machines. In recent years, container technology, which allows for the virtualization of individual processes, has become a popular virtualization technique. However, there is limited research into the scalability of these containers from both an energy efficiency and system performance perspective. This thesis aims to investigate this issue through large-scale benchmarking experiments. Results of the benchmarking experiments indicate that not necessarily the total amount of containers, but the assigned task of each individual container are relevant when considering energy efficiency. Key findings show a link between latency measurements performed by individual containers and allocated CPU cores on the host machine, with additional CPU cores causing a drop in latency as the amount of containers increase. Further, power consumption seems to hit its peak when CPU utilisation is only at 50%, with additional CPU utilisation causing no increase in power consumption. Finally, RAM utilisation seems to scale linearly with the total amount of containers involved.
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(2013)In Finland grain has to handle that seeds will stay in good condition in storage. The most common method of preservation is drying. 11 % of energy consumption in a grain growing chain is used in drying. EU has set the aim to achieve 9 % energy saving by year 2016 compared to average energy consumption in years 2001-2005. Ministry of agriculture and forestry has started energy program in agriculture, which aims to energy saving in agriculture. The aim of this study was to find out by computer simulation how to get the best energy efficiency in grain drying in different conditions. In the study was made a series of simulations to find out is different adjustments needed in different conditions. By sensitivity analysis was found out, which variable (condition or adjustment) affects most to the drying process. To find out reliability of the simulator energy consumption and drying time results was compared between simulation and real dryings in Viikki’s research farm. The best energy efficiency was achieved when high drying air temperature, fast grain circulation and small amount of air were used. The grain drying process is very sensitive to drying air temperature, moisture of grain and amount of air. The process is quite sensitive to density of grain and outside temperature. The simulator givesreliable results for energy consumption when grain moisture is more than 17% (w.b.) and for drying time when grain moisture is lower than 17 %. By adjusting grain drying process it is possible to save remarkable amount of energy. It is important to harvest and dry grain as good conditions as possible. Also isimportant to use isolation in dryer and maintain the burner.
Now showing items 1-8 of 8