Energy Information Systems: From the Basement to the Boardroom

A significant buildings energy reduction opportunity exists in the office sector, given that this market segment typically is an early adopter of new technology. There is a rising trend towards smart and connected offices through the internet of things (IoT) that provides new opportunities for operational efficiency and environmental sustainability practices. Leading commercial real estate companies have begun to shift from individual building automation systems (BAS) to partially integrated and automated systems such as energy information systems (EIS). In both the United States and India, organizations are seeking operational excellence, enhanced tenant relationships, and topline growth. Hence it is imperative to engage the executives with decision-making power, by tapping into their interest in sustainability, corporate social responsibility, and innovation. This expansion of interest can enable data-driven decisions, strong energy investments, and deeper energy benefits, and would drive innovation in this field. However, none of this would be possible without robust, consistent building energy information to provide visibility across all the levels of decision making, i.e. from the basement where the facilities staff take operational action to the boardroom where the executives make investment decisions. Price, security, and ease of use remain barriers to the adoption and pervasive use of promising EIS technologies in commercial office buildings. We believe that these barriers can be addressed through the development of ready, simplified, consistent, commercially available, low-cost EIS-in-a-box packages, that have a pre-defined set of hardware components and software features and functionality that are pertinent to a particular building sector. These simplified, sector-specific EIS packages can help to obviate the need for customization, and enhance ease of use, thereby enabling scale-up, in order to facilitate building energy savings. The EIS-in-a-box are adaptable in both U.S. and Indian office buildings, and potentially beyond these two countries

Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data

Knowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.TU Berlin, Open-Access-Mittel - 201

IoT-teknologian hyödyntäminen sähköverkko-omaisuuden hallinnassa

Objective of this thesis is to define and assess changes in energy sector, which will directly or indirectly affect distribution grid operation and management in Finland, and to determine measurable events or variables, which enable identification and monitoring of the recognized changes. Based on assessment of the upcoming changes, possibilities for utilizing IoT technologies in management and monitoring applications of the identified changes, are assessed. In the assessment of upcoming changes, total of eight subjects were covered and microgeneration, electric vehicles and heat pumps were identified to be the most probable changes to realistically penetrate Finnish energy sector within a time scope of approximately 10 years. However, none of the assessed, changes, were found to have significant and wide-scale effects in terms of performance of Finnish distribution networks. For utilization of IoT technologies in distribution networks one application for operational grid monitoring of power quality problems derived from residential photovoltaic generation, and three cases for IoT based asset health and condition monitoring were assessed. Furthermore, requirements and architecture for data storage and analysis platform of IoT based system were discussed. From the evaluated applications condition monitoring scheme of circuit breakers was determined to be the most promising alternative.Diplomityön tavoitteena on määritellä ja arvioida energiasektoriin vaikuttavien tulevien muutosten suoria tai epäsuoria vaikutuksia jakeluverkon toimintaan ja hallintaan. Havaittujen muutosten vaikutuksista on tarkoitus tunnistaa mitattavia ilmiöitä tai suureita, jotka mahdollistavat muutosten tunnistamisen sekä seurannan. Muutosanalyysiin pohjautuen tavoitteena on tunnistaa ja arvioida mahdollisuuksia IoT-teknologian hyödyntämiseksi havaittujen muutosten aiheuttamien ongelmakohtien tai mahdollisuuksien tunnistamisessa, seurannassa sekä hallinnassa. Energiasektoriin vaikuttavien muutosten analyysissä arvoitiin kokonaisuudessaan kahdeksaa eri aihealuetta ja lopputuloksena pientuotannon, sähköautojen sekä lämpöpumppujen todettiin olevan todennäköisimmät teknologiat, jotka yleistyvät merkittävissä määrin suomalaisessa sähköverkossa seuraavan kymmenen vuoden aikana. Minkään käsitellyn muutoskohdan ei kuitenkaan todettu aiheuttavan laajamittaisia ja merkittäviä ongelmia jakeluverkon toimintaan. IoT-teknologian hyödyntämiseen jakeluverkkotoiminnassa käsiteltiin yhtä verkon käyttöön ja sähkön laatuun liittyvää sovellusta, jonka avulla hajautetun pienaurinkotuotannon vaikutuksia pystytään seuraamaan, sekä lisäksi kolmeen eri verkkokomponenttiin kohdistuvaa jatkuvan kunnon seurannan sovellusta. Tämän lisäksi IoT-järjestelmän toteuttamiseksi vaadittavalle analyysi- ja tietojärjestelmäalustalle määriteltiin rakenteellisia ja toiminnallisia tarpeita. Työssä käsitellyistä IoT-sovelluksista lupaavimmaksi todettiin katkaisijoihin kohdistuva jatkuvan kunnonhallinnan sovellus

Big data analytics in smart grids: a review

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ANOMALY INFERENCE BASED ON HETEROGENEOUS DATA SOURCES IN AN ELECTRICAL DISTRIBUTION SYSTEM

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as well as its potential to relate to other feeders from other utilities. The distributed generation has been part of the smart grid mission, the addition can be prone to electronic manipulation. This dissertation provides a comprehensive establishment in the emerging platform where the computing resources have been ubiquitous in the electrical distribution network. The topics covered in this thesis is wide-ranging where the anomaly inference includes load modeling and profile enhancement from other sources to infer of topological changes in the primary distribution network. While metering infrastructure has been the technological deployment to enable remote-controlled capability on the dis-connectors, this scholarly contribution represents the critical knowledge of new paradigm to address security-related issues, such as, irregularity (tampering by individuals) as well as potential malware (a large-scale form) that can massively manipulate the existing network control variables, resulting into large impact to the power grid

Exploratory approach for network behavior clustering in LoRaWAN

The interest in the Internet of Things (IoT) is increasing both as for research and market perspectives. Worldwide, we are witnessing the deployment of several IoT networks for different applications, spanning from home automation to smart cities. The majority of these IoT deployments were quickly set up with the aim of providing connectivity without deeply engineering the infrastructure to optimize the network efficiency and scalability. The interest is now moving towards the analysis of the behavior of such systems in order to characterize and improve their functionality. In these IoT systems, many data related to device and human interactions are stored in databases, as well as IoT information related to the network level (wireless or wired) is gathered by the network operators. In this paper, we provide a systematic approach to process network data gathered from a wide area IoT wireless platform based on LoRaWAN (Long Range Wide Area Network). Our study can be used for profiling IoT devices, in order to group them according to their characteristics, as well as detecting network anomalies. Specifically, we use the k-means algorithm to group LoRaWAN packets according to their radio and network behavior. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Quite important is the fact that LoRaWAN captures, via the wireless interface, packets of multiple operators. Indeed our analysis was performed on 997, 183 packets with 2169 devices involved and only a subset of them were known by the considered operator, meaning that an operator cannot control the whole behavior of the system but on the contrary has to observe it. We were able to analyze clusters’ contents, revealing results both in line with the current network behavior and alerts on malfunctioning devices, remarking the reliability of the proposed approach