Remote Control and Monitoring of Smart Home Facilities via Smartphone with Wi-Fly

Due to the widespread ownership of smartphone devices, the application of mobile technologies to enhance the monitoring and control of smart home facilities has attracted much academic attention. This study indicates that tools already in the possession of the end user can be a significant part of the specific context-aware system in the smart home. The behaviour of the system in the context of existing systems will reflect the intention of the client. This model system offers a diverse architectural concept for Wireless Sensor Actuator Mobile Computing in a Smart Home (WiSAMCinSH) and consists of sensors and actuators in various communication channels, with different capacities, paradigms, costs and degree of communication reliability. This paper focuses on the utilization of end users’ smartphone applications to control home devices, and to enable monitoring of the context-aware environment in the smart home to fulfil the needs of the ageing population. It investigates the application of an iPhone to supervise smart home monitoring and control electrical devices, and through this approach, after initial setup of the mobile application, a user can control devices in the smart home from different locations and over various distances

Enabling Micro-level Demand-Side Grid Flexiblity in Resource Constrained Environments

The increased penetration of uncertain and variable renewable energy presents various resource and operational electric grid challenges. Micro-level (household and small commercial) demand-side grid flexibility could be a cost-effective strategy to integrate high penetrations of wind and solar energy, but literature and field deployments exploring the necessary information and communication technologies (ICTs) are scant. This paper presents an exploratory framework for enabling information driven grid flexibility through the Internet of Things (IoT), and a proof-of-concept wireless sensor gateway (FlexBox) to collect the necessary parameters for adequately monitoring and actuating the micro-level demand-side. In the summer of 2015, thirty sensor gateways were deployed in the city of Managua (Nicaragua) to develop a baseline for a near future small-scale demand response pilot implementation. FlexBox field data has begun shedding light on relationships between ambient temperature and load energy consumption, load and building envelope energy efficiency challenges, latency communication network challenges, and opportunities to engage existing demand-side user behavioral patterns. Information driven grid flexibility strategies present great opportunity to develop new technologies, system architectures, and implementation approaches that can easily scale across regions, incomes, and levels of development

A Comprehensive Review of Smart Energy Meters: An Innovative Approach

Energy meter is an important device used for measuring the power. It is used in customers� homes, industries etc. for measuring the electrical power. A lot of modifications and development has taken place in the construction and operation of the energy meters over a decade. In view of above this paper presents a review of the development of the energy meters and their applications. Energy meters and its different types along with the innovation in this field is been discussed in this paper

Implications of Data Sampling Resolution on Water Use Simulation, End-Use Disaggregation, And Demand Management

Understanding the tradeoff between the information of high-resolution water use data and the costs of smart meters to collect data with sub-minute resolution is crucial to inform smart meter networks. To explore this tradeoff, we first present STREaM, a STochastic Residential water End-use Model that generates synthetic water end-use time series with 10-s and progressively coarser sampling resolutions. Second, we apply a comparative framework to STREaM output and assess the impact of data sampling resolution on end-use disaggregation, post meter leak detection, peak demand estimation, data storage, and meter availability. Our findings show that increased sampling resolution allows more accurate end-use disaggregation, prompt water leakage detection, and accurate and timely estimates of peak demand. Simultaneously, data storage requirements and limited product availability mean most large-scale, commercial smart metering deployments sense data with hourly, daily, or coarser sampling frequencies. Overall, this work provides insights for further research and commercial deployment of smart water meters

Wireless mains sensor for monitoring domestic energy consumption

Abstract. Past studies have shown that awareness of energy consumption can lead to reduction in electricity usage and that real-time, per-appliance data on electricity consumption would provide greater utility and actionable information. Yet, the customers of today’s utility companies typically have to be content with data that is aggregated, delayed and difficult to access. Comprehensive real-time data would also aid in optimizing energy consumption with respect to dynamic pricing and avoiding peak consumption periods. The objective of this thesis was to design and manufacture a wireless sensor for continuous and real-time metering of the energy consumption of a household in the UBI-AMI system version 2. The resulting Mains sensor reads the total energy consumption from the kilowatt hour meter using either a galvanic or an optical connection. The individual loads of the fuses in the circuit breaker panel are measured with Hall sensors. An 8-bit microcontroller collects analog measurements, conducts 10-bit ADC and transmits the resulting digital data to the UBI-AMI system using a commercial 6LoWPAN radio module and the CoAP protocol. The data enables the differentiation of the energy consumption of integrated and built-in elements such as floor heating and sauna from the total energy consumption of the household. The Mains sensor was tested with a demonstrator that comprised of a fuse board, a kilowatt hour meter and sockets for connecting loads. The Mains sensor was found to be flawless in reading the total energy consumption from the kilowatt hour meter using a galvanic connection. The sensor was able to read 84% of fast pulses and showed 4% surplus with slow pulses if the optical connection was used. The Hall sensors had a maximum average error of 0.47% with an active power, in comparison to a commercial energy meter. These results show that the Mains sensor provides sufficiently accurate and reliable information for improving the awareness of energy consumption of a household.Langaton sähköpäätaulusensori kotitalouden energiankulutuksen seuraamiseen. Tiivistelmä. Tutkimusten mukaan tietoisuus energiankulutuksesta voi johtaa sähkön käytön vähenemiseen, ja että tosiaikainen, laitekohtainen kulutustieto olisi hyödyllisempää. Silti nykyisin sähköyhtiöiden asiakkaiden täytyy tyypillisesti tyytyä kulutustietoihin, jotka on kerätty kokonaiskulutuksesta, ovat käytettävissä viiveellä, ja joihin on vaikea päästä käsiksi. Kattava tosiaikainen informaatio myös auttaisi huippukulutuskausien välttämisessä ja energiankulutuksen optimoinnissa dynaamisen hinnoittelun suhteen. Tämän diplomityön tavoitteena oli suunnitella ja valmistaa langaton sensori kotitalouden energiankulutuksen jatkuvaan ja tosiaikaiseen mittaukseen osana UBI-AMI-järjestelmän versiota 2. Syntynyt sähköpäätaulusensori lukee kokonaisenergiankulutuksen kilowattituntimittarista joko galvaanista tai optista yhteyttä käyttäen. Yksittäiset ryhmäkohtaiset kuormat mitataan sulaketaulusta Hallin antureilla. 8-bittinen mikrokontrolleri kerää analogiset mittaukset ja muuntaa ne digitaaliseksi dataksi, joka lähetetään UBI-AMI-järjestelmälle käyttäen kaupallista 6LoWPAN-radiomoduulia ja CoAP-protokollaa. Mittausdata mahdollistaa integroitujen ja kiinteästi asennettujen sähkölaitteiden, esimerkiksi lattialämmityksen ja saunan, energiankulutuksen eriyttämisen kotitalouden kokonaiskulutuksesta. Sähköpäätaulusensorin toiminta arvioitiin testilaitteistolla, joka koostui sulaketaulusta, kilowattituntimittarista ja pistorasioista kuormien liittämistä varten. Sähköpäätaulusensorin havaittiin lukevan kokonaisenergiankulutuksen kilowattituntimittarista virheettömästi galvaanista yhteyttä käyttäen. Optista yhteyttä käytettäessä sensori kykeni lukemaan 84 % nopeista pulsseista ja hitaat pulssit saivat sensorin mittaamaan käytetyn energian 4% todellista suuremmaksi. Hallin antureilla suurin keskimääräinen virhe kaupalliseen mittariin verrattuna oli 0,47 % pätötehollisella kuormalla. Tulosten perusteella sähköpäätaulusensori antaa riittävän tarkkaa ja luotettavaa tietoa energiankulutuksesta ja sitä voidaan käyttää energiankulutuksen tietoisuuden lisäämiseen kotitalouksissa

Disaggregating high-resolution gas metering data using pattern recognition

© 2018 Elsevier B.V. Growing concern about the scale and extent of the gap between predicted and actual energy performance of new and retrofitted UK homes has led to a surge in the development of new tools and technologies trying to address the problem. A vital aspect of this work is to improve ease and accuracy of measuring in-use performance to better understand the extent of the gap and diagnose its causes. Existing approaches range from low cost but basic assessments allowing very limited diagnosis, to intensively instrumented experiments that provide detail but are expensive and highly disruptive, typically requiring the installation of specialist monitoring equipment and often vacating the house for several days. A key challenge in reducing the cost and difficulty of complex methods in occupied houses is to disaggregate space heating energy from that used for other uses without installing specialist monitoring equipment. This paper presents a low cost, non-invasive approach for doing so for a typical occupied UK home where space heating, hot water and cooking are provided by gas. The method, using dynamic pattern matching of total gas consumption measurements, typical of those provided by a smart meter, was tested by applying it to two occupied houses in the UK. The findings revealed that this method was successful in detecting heating patterns in the data and filtering out coinciding use