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The energy sector is in transition–being forced to rethink the current practice and apply data-management based IT solutions to provide a scalable and sustainable supply and distribution of energy. Novel challenges range from renewable energy production over energy distribution and monitoring to controlling and moving energy consumption. Huge amounts of “Big Energy Data,” i.e., data from smart meters, new renewable energy sources (RES–such as wind, solar, hydro, thermal, etc), novel distributions mechanisms (Smart Grid), and novel types of consumers and devices, e.g., electric cars, are being collected and must be managed and analyzed to yield their potential.

Advanced Building Energy Data Visualization is a way to detect performance problems in commercialbuildings. By placing sensors in a building that collects data from example, air temperature and electricalpower, then makes it possible to calculate the data in Data Visualization software. This softwaregenerates visual diagrams so the building manager or building operator can see if for example thepower consumption is to high.A first step (before sensors are installed in a building) to see how the energy consumption is in abuilding can be to use a Benchmarking Tool. There is a number of Benchmarking Tools that is availablefor free on the Internet. Each tool have a bit different approach, but they all show how much energyconsumption there is in a building compared to other similar buildings.In this study a new web design for the benchmarking tool CalARCH has been developed. CalARCHis developed at the Berkeley Lab in Berkeley, California, USA. CalARCH uses data collected only frombuildings in California, and is only for comparing buildings in California with other similar buildingsin the state.Five different versions of the web site were made. Then a web survey was done to determine whichversion would be the best for CalARCH. The results showed that Version 5 and Version 3 was the best.Then a new version was made, based on these two versions. This study was made at the LawrenceBerkeley Laboratory.

The Energy Industry utilizes Energy Management Information Systems (EMIS) smart meters to monitor utility consumers’ energy consumption, communicate energy consumption information to consumers, and to collect a plethora of energy consumption data about consumer usage. The EMIS energy consumption information is typically presented to utility consumers via a smart meter web portal. The hope is that EMIS web portal use will aid utility consumers in managing their energy consumption by helping them make effective decisions regarding their energy usage. However, little research exists that evaluates the effectiveness or success of an EMIS smart meter web portal from a utility consumer perspective. The research goal was to measure EMIS smart meter web portal success based on the DeLone and McLean Information Success Model. The objective of the study was to investigate the success constructs system quality, information quality, service quality, use, and user satisfaction, and determine their contribution to EMIS success, which was measured as net benefits. The research model used in this study employed Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) to determine the validity and reliability of the measurement model and to evaluate the hypothetical relationships in the structural model. The significant validity and reliability measures obtained in this study indicate that the DeLone and McLean Information Success Model (2003) has the potential for use in future EMIS studies. The determinants responsible for explaining the variance in net benefits were EMIS use and user satisfaction. Based on the research findings, several implications and future research are stated and proposed.

While more efficient use of energy is increasingly vital to the development of the modern industrialized world, emerging visualization tools and approaches of telling data stories provide an opportunity for the exploration of a wide range of topics related to energy consumption and conservation (Olsen, 2017). Telling energy stories using data visualization has generated great interest among journalists, designers and scientific researchers; over time it has been proven to be effective to provide knowledge and insights (Holmes, 2007). This thesis proposes a new angle of tackling the challenge of designing visualization experience for building energy data, which aims to invite the users to think besides the established data narratives, augment the knowledge and insight of energy-related issues, and potentially trigger ecological responsible behaviors, by investigating and evaluating the efficacy of the existing interactive energy data visualization projects, and experimenting with user-centric interactive interface and unusual visual expressions though the development of a data visualization prototype.

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities.

The thesis concerns mainly the construction of a pipeline that enables the analyzing,visualizing, and forecasting of time-series data in an intuitive and streamlined process.The main data set consists of four measurements: purchased heating, purchasedelectricity, and purchased cold water from select buildings at the Lule ̊a Universityof Technology campus as well as the outside temperature of the campus. This isused to establish a proof of concept, demonstrating the validity of this pipeline andits subsystems. Using this pipeline you are able to upload data, visualize data andanalyze data online and also create future forecasts of these measurements which arealso displayed online.As the global demand for energy efficiency increases, tools, like this one, is moreimportant than ever in order to give the decision-makers more insight. In the caseof the campus buildings, you might be able to more easily identify anomalous valueswhich point to some oversight that can then be amended. For instance, if two identicalbuildings exist and one of them consumes 50% more heating, you can conclude thata problem exists and now you know where the problem lies so you can amend theissue.Forecasting future consumption is also helpful since it would allow you to reducethe purchasing of fossil fuels, such as gas, which is the case at La Trobe Universityin Melbourne. Using forecasting they can better predict how much gas they need topurchase and when the peak consumption hours are so that they can adjust their solarproduction accordingly. Thus, forecasting future consumption can further reduce theglobal need and impact of fossil fuels.To conclude, this pipeline can be used as a tool to reduce the environmental impactof the Lule ̊a University of Technology campus buildings. The pipeline can then beapplied to other areas to help them solve their problems. Some of the findings ofthis thesis include comparisons of common forecasting algorithms and the benefits ofusing weekday/weekend models.In the future, this might also inspire others to make similar projects, just like LaTrobe University inspired us at the Lule ̊a University of Technology.

This project applies statistical inference methods to energy data from the island of Luzon in the Philippines. The goal of the project is to explore different ways of creating predictive models and to understand the assumptions that are made about reality when a certain model is selected. The main models discussed in the project are Simple Linear Regression and Markov Chain Models. The predictions were used to assess Luzon's progress towards the sustainable development goals. All models considered in this project suggest that they are not on target to meet the sustainability goal.

This panel paper aims at initiating discussion at the Second International Workshop on Energy Data Management (EnDM 2013) about the important research challenges within Energy Data Management. The authors are the panel organizers, extra panelists will be recruited before the workshop.

In many US homes, 15% of the energy that can be saved is hidden beneath complex mathematical calculations. Hidden energy savings can be revealed by converting mathematical calculations to data visualizations, creating a story for residents to see how they are consuming energy. Cloud-based data visualization platforms offer the ability to appropriately communicate complex building energy data to a broad set of stakeholders. Unfortunately, proprietary solutions are too expensive and open-source options lack standardization for cloud-based energy monitoring. This study aims to create a comprehensive energy dashboard display to increase residents' energy awareness of how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight visualization chart types that are used on utility bills and energy monitoring devices. In addition, the literature review uncovered eight additional visualization chart types that have the functionality to visualize energy data. Next, the visualization chart types were combined with data modeling design techniques to create prototype energy dashboard displays to communicate energy insights to residents. Soon utility companies will begin to provide data visualizations for the majority of their customers. The insights from this study can help to inform and lead the development of commercially used data visualizations. In addition, this research can provide utility companies with a blueprint on how to share energy consumption data with customers. / Master of Science / For residents to live an energy-efficient lifestyle, they must first begin by learning about one's energy consumption behaviors in the home. Unfortunately, utility bills miss out on communicating energy insights to customers based on how the energy data appears on the utility bill. Graphs on utility bills that display aggregate monthly energy consumption do not provide enough information for residents to comprehend how energy is consumed through their homes or provide information on how to lower energy consumption. There are commercial energy consumption devices on the market such as CURB and eGauge that provide an energy dashboard display, but the visuals are too complex to draw conclusions. This study aims to create an energy dashboard display that allows residents to see how energy is consumed throughout their homes. But before energy dashboards can be created, a content analysis of current visualization chart types used on utility bills and energy monitoring devices were discovered to see how energy data has been visualized in the energy domain. Next, a literature review was conducted to reveal other visualization chart types outside of the energy domain that could be used to visualize energy data. The content analysis results identified eight chart types used of utility bills and energy monitoring devices. In addition, the literature review results uncovered eight additional chart types not used on utility bills and energy monitoring devices that have the potential to visualize energy data. Next, the identified and uncovered chart types were combined with data modeling design techniques to create example energy dashboard displays. Changing the way energy data is displayed to residents, can educate residents on how energy is consumed throughout their home. In addition, the insights from this study can provide utility companies with a model for displaying energy data to increase their customers' energy awareness. Living an energy-efficient lifestyle, first began by understanding how energy is consumed throughout one's home.