Household occupancy monitoring using electricity meters

Occupancy monitoring (i.e. sensing whether a building or room is currently occupied) is required by many building au-tomation systems. An automatic heating system may, for ex-ample, use occupancy data to regulate the indoor temperature. Occupancy data is often obtained through dedicated hardware such as passive infrared sensors and magnetic reed switches. In this paper, we derive occupancy information from elec-tric load curves measured by off-the-shelf smart electricity meters. Using the publicly available ECO dataset, we show that supervised machine learning algorithms can extract occu-pancy information with an accuracy between 83 % and 94%. To this end we use a comprehensive feature set containing 35 features. Thereby we found that the inclusion of features that capture changes in the activation state of appliances provides the best occupancy detection accuracy

Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy

Indiana University-Purdue University Indianapolis (IUPUI)Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control

A review of the Digital Twin technology for fault detection in buildings

publishedVersio

A model personal energy meter

Every day each of us consumes a significant amount of energy, both directly through transport, heating and use of appliances, and indirectly from our needs for the production of food, manufacture of goods and provision of services. This dissertation investigates a personal energy meter which can record and apportion an individual's energy usage in order to supply baseline information and incentives for reducing our environmental impact. If the energy costs of large shared resources are split evenly without regard for individual consumption each person minimises his own losses by taking advantage of others. Context awareness offers the potential to change this balance and apportion energy costs to those who cause them to be incurred. This dissertation explores how sensor systems installed in many buildings today can be used to apportion energy consumption between users, including an evaluation of a range of strategies in a case study and elaboration of the overriding principles that are generally applicable. It also shows how second-order estimators combined with location data can provide a proxy for fine-grained sensing. A key ingredient for apportionment mechanisms is data on energy usage. This may come from metering devices or buildings directly, or from profiling devices and using secondary indicators to infer their power state. A mechanism for profiling devices to determine the energy costs of specific activities, particularly applicable to shared programmable devices is presented which can make this process simpler and more accurate. By combining crowdsourced building-inventory information and a simple building energy model it is possible to estimate an individual's energy use disaggregated by device class with very little direct sensing. Contextual information provides crucial cues for apportioning the use and energy costs of resources, and one of the most valuable sources from which to infer context is location. A key ingredient for a personal energy meter is a low cost, low infrastructure location system that can be deployed on a truly global scale. This dissertation presents a description and evaluation of the new concept of inquiry-free Bluetooth tracking that has the potential to offer indoor location information with significantly less infrastructure and calibration than other systems. Finally, a suitable architecture for a personal energy meter on a global scale is demonstrated using a mobile phone application to aggregate energy feeds based on the case studies and technologies developed

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Contextualising water use in residential settings: a survey of non-intrusive techniques and approaches

Water monitoring in households is important to ensure the sustainability of fresh water reserves on our planet. It provides stakeholders with the statistics required to formulate optimal strategies in residential water management. However, this should not be prohibitive and appliance-level water monitoring cannot practically be achieved by deploying sensors on every faucet or water-consuming device of interest due to the higher hardware costs and complexity, not to mention the risk of accidental leakages that can derive from the extra plumbing needed. Machine learning and data mining techniques are promising techniques to analyse monitored data to obtain non-intrusive water usage disaggregation. This is because they can discern water usage from the aggregated data acquired from a single point of observation. This paper provides an overview of water usage disaggregation systems and related techniques adopted for water event classification. The state-of-the art of algorithms and testbeds used for fixture recognition are reviewed and a discussion on the prominent challenges and future research are also included

Wireless Sensor Network para mejorar la eficiencia energética en hogares y pymes y su integración en la Smart Grid

[SPA] Durante los últimos años los ámbitos tecnológicos de la Redes de Sensores Inalámbricas – WSN –y la aplicación de las tecnologías de la información y comunicación sobre la redes eléctricas – Smart Grid – se han convertido en importantes campos de interés científico. De igual forma, la mejora de la Eficiencia Energética se ha convertido en una necesidad de la sociedad actual, debido al volumen de crecimiento de la población, y por lo tanto la demanda, y la dependencia energética actual de los combustibles fósiles. Esta Tesis analiza en profundidad las diferentes posibilidades de aplicación de las WSN en el control energético de bajo voltaje, a través del diseño e implementación de un sistema, de bajo coste y de fácil implantación, capaz de optimizar el consumo energético en hogares y pymes. El sistema descrito cuenta con una unidad de almacenamiento energético de respaldo que ayuda a minimizar la potencia pico requerida en la instalación, ayudando a balancear la curva de consumo del hogar o empresa. Este sistema opera conjuntamente con la WSN como un nodo más, haciendo la función de Gateway con la nube a la vez que aporta control e información a los usuarios de forma que estos puedan adoptar medidas por su parte en relación al uso que hacen de la energía. Para dotar al sistema de capacidad de interoperación con otros sistemas y hacerlo accesible desde Internet en general se le ha dotado de capacidad de comunicación basada en IPv6 haciendo que la red pase a formar parte de lo que se ha dado a conocer como Internet de las Cosas – Internet of Things, IoT–. De igual forma se expone la posibilidad de realizar una comunicación bidireccional con la productora eléctrica de forma que se obtenga información sobre las variaciones del precio de la electricidad que se consume, así como la posibilidad de reportar previsiones y modelos de demanda en tiempo real a la misma de forma que se acerca al concepto de Demanda Gestionada por el Hogar – Home Energy Management, HEM –.[ENG] In last years the technological knowledge areas of Wireless Sensor Networks – WSN – and the Smart Grid – Information and Communications Technology application over the electrical transportation network – became scientific areas of great interest. Similarly, the improvement on Energy Efficiency became a necessity in actual social context, due to the high population growth volume, and therefore the energy demand, and the actual energy dependency on fossil fuels. This Thesis deeply analyses the different possibilities of WSN application on the low voltage energy control, through the design and implementation of a low cost system, easy to deploy, and able to optimise the energy consumption in households and SMEs. The described system has a back-up energy storage unit that helps to minimise the peak power required in the installation. It also helps to balance the demand curve in the household or SME. This system jointly operates with the WSN as a extra node, making the role of Gateway with the cloud, at the same time it provides control capabilities and information to final users. This give final users the capacity of take measures relatives to the way they use the energy. The system has a communication protocol based in IPv6 in order to provide it with interoperation capacity with other system and with access from Internet devices. In this way, all the nodes in WSN are included in the Internet of Things. Finally, the possibility of perform a bidirectional communication with the Utility to obtain prices changes information from the Utility and the possibility of report back foreseen and demand models in real time to the Utility. It approaches the system to the concept of Home Energy Management –HEM –.Escuela Internacional de DoctoradoUniversidad Politécnica de CartagenaPrograma de Doctorado Tecnologías de la Información y las Comunicacione

Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems

State-of-the-art fault detection methods are equipment and domain specific and non-comprehensive. As a result, the applicability of these methods in different domains is very limited and they can achieve significant levels of performance by having knowledge of the domain and the ability to mimic human thinking in identifying the source of a fault with a comprehensive knowledge of the system and its surroundings. This technical report presents a comprehensive semantic framework for fault detection and diagnostics (FDD) in systems simulation and control. Our proposed methodology entails of implementation of the knowledge bases for FDD purposes through the utilization of ontologies and offers improved functionalities of such system through inference-based reasoning to derive knowledge about the irregularities in the operation. We exercise the proposed approach by working step by step through the setup and solution of a fault detection and diagnostics problem for a small-scale heating, ventilating and air-conditioning (HVAC) system.NIS

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted