Energy monitoring systems (EMS) are crucial in improving energy eciency to encourage sustainable development. Such systems usually come with an open sandbox experience to provide multiple tools for monitoring, analytic, and reporting purposes, but this also creates user experience (UX) issues. EMS and energy dashboards have been well researched from a technical perspective while simplification and limitation are also thoroughly studied within HumanComputer Interaction (HCI). However, few have applied the HCI literature to the EMS field, especially in solving the UX issues that the open sandbox experience creates. Therefore, this study aims to bridge this gap and study how to improve an EMS with an open sandbox experience through simplification and limitation. In what follows, the discovery and definition of UXissues in the system through pre-study, interview study, and user evaluation are presented. A prototype was also developed with the help of HCI literature to solve these issues, and was evaluated with the participants. The results show improvements in participants’ task performance and the proposed redesign received positive feedback. In the discussion, key considerations for the design of EMS systems are raised, as well as the sustainability eects of this study and possible future directions. / Energiuppföljningssystem (EMS) är avgörande för att förbättra energieektivitet och på så vis bidra till en hållbar omställning av samhället. Sådana system har ofta en öppen design för att tillhandahålla en stor bredd av analysverktyg, men detta skapar också problem med användarupplevelsen. EMS har i literaturen undersökts väl ur ett tekniskt perspektiv, och designprinciper så som förenkling och begränsning har också studerats grundligt inom området för människadatorinteraktion. Dock finns det få studier som har använt dessa principer för att förbättra energiuppföljningssystem i synnerhet, särskilt för att lösa användbarhets-problem som en öppen design skapar. Därför syftar denna studie till att undersöka detta genom att omarbeta och förbättra ett EMS med en öppen design lösning med hjälp av designprinciperna förenkling och begränsning. Genom en förstudie, en intervjustudie samt en användarutvärdering har användbarhesproblem identifierats i systemet. En prototyp togs också fram med stöd i litteraturen för att lösa dessa problem, och den utvärderades senare med deltagarna. Resultaten visar en förbättring i deltagarnas uppgiftsutförande och den förbättrade designen fick positiv feedback. I diskussionen beskrivs ett antal insikter relevant för design av energiuppföljningssystem, samt diskuterar olika hållbarhetsaspekter vid denna studie och framtida ämnen att utforska.

In this MSc thesis, it is investigated what parameters are relevant for describing energy consumption of heavy haul freight trains and how these can be used to develop key performance indicators (KPIs) for energy efficiency. The possible set of KPI is bounded by data available from energy meters used in electric IORE class locomotives hauling iron ore trains in northern Sweden. Furthermore, the analysis is only concerned with energy efficiency at the rolling stock level, excluding losses in the electric power supply network. Based on a literature study, parameters of interest describing driver, operations and rolling stock energy efficiency have been identified. By means of simulation, a parametric study is performed, simulating a 30 ton axle load iron ore train with 68 wagons. Train modelling input is obtained from technical documentation or estimated through measurements and statistical analysis. A multi-particle representation of the train is used to calculate gradient resistance for the simulation, which is also applied to determine the curve resistance.  Results show that the motion resistance is simulated quite accurately, while the lack of a driver model in the simulation tool leads to overestimation of energy consumption. Taking this into account, the importance of the driver for energy efficiency can still clearly be showcased in the parametric study. Especially on long steep downhill sections, prioritising the electric brakes over mechanical brakes is demonstrated to have a huge influence on net energy consumption, as has the amount of coasting applied. With the same driver behaviour in all simulations, the savings in specific energy from increasing axle load to 32.5 tons is estimated. Moreover, a comparison of increased train length and axle load points towards higher savings for the latter. In the end, parametric study results are used to recommend a structure for a monitoring system of energy efficiency based on a set of KPIs. With a sufficiently high sampling rate of energy meter data, it is adequate for calculating driver related KPIs and some additional KPIs. More KPIs can be tracked with access to additional data, e.g. cargo load. / I detta examensarbete undersöks vilka parametrar som är relevanta för att beskriva energiförbrukning för tunga godståg och hur dessa kan nyttjas för att utveckla nyckeltal för energieffektivitet. Antalet möjliga nyckeltal avgränsas till sådana som kan beräknas med data från elmätare som används i elektriska littera IORE lok som drar tunga malmtåg i norra Sverige. Vidare så tar analysen endast hänsyn till energieffektivitet för rullande materiel, vilket utesluter förluster i elektriska kraftmatningsnätet. Baserad på en litteraturstudie har relevanta parametrar som beskriver förare, drift och rullande materiel identifierats. Med hjälp av simuleringar av ett malmtåg med 30 tons axellast och 68 vagnar så utförs en parameterstudie. Indata för tågmodelleringen erhålls från teknisk dokumentation respektive uppskattas genom mätningar och statistisk analys. En representation av tåget som flertalet partiklar tillämpas i simulering för att beräkna lutningsmotståndet. Dessutom används densamma för att ta fram kurvmotståndet. Resultaten visar att gångmotstånd simuleras ganska exakt, medan avsaknad av en förarmodell i simuleringsvertyget leder till överskattad energiförbrukning. Med hänsyn tagen till detta så kan betydelsen av föraren för energieffektivitet fortfarande påvisas mycket tydligt i parameterstudien. I synnerhet i långa branta nedförsbackar har prioritering av den elektriska bromsen framför den mekaniska bromsen mycket stor påverkan på nettoenergiförbrukningen, likaväl som hur mycket tåget frirullar. Med samma förarbeteende i samtliga simuleringar har besparingar i specifik energiförbrukning kunnat uppskattats för en ökning av axellasten till 32,5 ton. Dessutom pekar en jämförelse av ökad tåglängd och axellast mot att sistnämnda ger större besparingar. Slutligen så har resultaten från parameterstudien nyttjats för att rekommendera en struktur för ett uppföljningssystem av energieffektivitet baserad på en uppsättning av nyckeltal. Med tillräckligt hög samplingsfrekvens på data från elmätare är den adekvat för att beräkna vissa nyckeltal, framförallt relaterad till förare. Fler nyckeltal kan följas upp med mer tillgänglig data så som lastvikter.

<p><strong>The Energy Monitoring Framework</strong>, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology.</p> <p>The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely.</p> <p>The main objectives of the project include the following:</p> <ul> <li>Set up a wireless network using sensors and smart implants with a base station/ controller.</li> <li>Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency.</li> <li>Set up a generalized interface to collect and process the sensor data values and store the data in a database.</li> <li>Design and develop a generic database compatible with various companies irrespective of the type and size.</li> <li> Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment.</li> </ul> <p>The General Structure of the project constitutes the following components:</p> <ul> <li>A wireless sensor network with a base station.</li> <li>An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators.</li> <li>A cloud that hosts a database and an API to collect and store information.</li> <li>A web application hosted in the cloud to provide an interactive platform for users to analyze the data.</li> </ul> <p>The project was demonstrated in:</p> <ul> <li>Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/).</li> <li>Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/).</li> <li>A company in Indiana.</li> </ul> <p>The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.</p> <p><br></p>

Indiana University-Purdue University Indianapolis (IUPUI) / The Energy Monitoring Framework, designed and developed by IAC, IUPUI, aims to provide a cloud-based solution that combines business analytics with sensors for real-time energy management at the plant level using wireless sensor network technology. The project provides a platform where users can analyze the functioning of a plant using sensor data. The data would also help users to explore the energy usage trends and identify any energy leaks due to malfunctions or other environmental factors in their plant. Additionally, the users could check the machinery status in their plant and have the capability to control the equipment remotely. The main objectives of the project include the following: • Set up a wireless network using sensors and smart implants with a base station/ controller. • Deploy and connect the smart implants and sensors with the equipment in the plant that needs to be analyzed or controlled to improve their energy efficiency. • Set up a generalized interface to collect and process the sensor data values and store the data in a database. • Design and develop a generic database compatible with various companies irrespective of the type and size. • Design and develop a web application with a generalized structure. Hence the database can be deployed at multiple companies with minimum customization. The web app should provide the users with a platform to interact with the data to analyze the sensor data and initiate commands to control the equipment. The General Structure of the project constitutes the following components: • A wireless sensor network with a base station. • An Edge PC, that interfaces with the sensor network to collect the sensor data and sends it out to the cloud server. The system also interfaces with the sensor network to send out command signals to control the switches/ actuators. • A cloud that hosts a database and an API to collect and store information. • A web application hosted in the cloud to provide an interactive platform for users to analyze the data. The project was demonstrated in: • Lecture Hall (https://iac-lecture-hall.engr.iupui.edu/LectureHallFlask/). • Test Bed (https://iac-testbed.engr.iupui.edu/testbedflask/). • A company in Indiana. The above examples used sensors such as current sensors, temperature sensors, carbon dioxide sensors, and pressure sensors to set up the sensor network. The equipment was controlled using compactable switch nodes with the chosen sensor network protocol. The energy consumption details of each piece of equipment were measured over a few days. The data was validated, and the system worked as expected and helped the user to monitor, analyze and control the connected equipment remotely.