Electrical consumption data contain a wealth of information, and their collection at scale is facilitated by the deployment of smart meters. Data collected this way is an aggregation of the power demands of all appliances within a building, hence inferences on the operation of individual devices cannot be drawn directly. By using methods to disaggregate data collected from a single measurement location, however, appliance-level detail can often be reconstructed. A major impediment to the improvement of such disaggregation algorithms lies in the way they are evaluated so far: Their performance is generally assessed using a small number of publicly available electricity consumption data sets recorded from actual buildings. As a result, algorithm parameters are often tuned to produce optimal results for the used datasets, but do not necessarily generalize to different input data well.
Instead of attempting to compile a benchmarking corpus from existing data sets, we present a methodological way to synthetically create data sets of definable disaggregation complexity. A high degree of realism can be accomplished by using accurate models of existing appliances and user activities. By forwarding synthetically generated data of gradually increasing levels of concurrent appliance activity to state-of-the-art disaggregation algorithms, we determine their sensitivity to specific data characteristics in a much more fine-grained way.
ANTgen – the AMBAL-based NILM Trace generator
We present a toolchain, ANTgen, that generates synthetic macroscopic load signatures for their use in conjunction with NILM (load disaggregation) tools. By default, it runs in scripted mode (i.e., with no graphical user interface) and processes an input configuration file into a set of CSV output files containing power consumption values and the timestamps of their occurrence, as well as a file summarizing the events that have occurred during the simulation). If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:
What do Austrians think about renewable energy technologies and related topics?
This question is examined annually by Prof. Nina Hampl and Dr. Robert Sposato in collaboration with Deloitte and Wien Energie in the Renewable Energies In Austria report series. At the core of this report lies a representative survey of over 1,000 participants conducted most recently in autumn 2019. Two clear signals emerged in this year’s report: a high level of acceptance for renewable energy technologies in general and broad support for climate policy measures.
As has been shown in the years before, the population holds a generally positive attitude towards renewable energy technologies: 77 % of the older respondents questioned are clearly in favour of building renewable energy technologies in their community. A number that is even higher among young respondents with 82 %. More specifically, photovoltaic power plants receive the broadest approval with 88%, followed by small hydropower with 74% and wind power with 67%.
An equally positive result was shown with respect to energy communities: Already around two thirds of the Austrian respondents are considering active participation in such communities, which allow private individuals to generate, consume, store and sell electricity or heat together. Austrian consumers attach particular importance to the fact that energy is generated locally and on the basis of renewable energy sources.
Albeit a little downslope from 2018 the group of potential electric car buyers also remains at a good level with 44 % considering to buy an electric vehicle as their next car. Again at 59 % young adults are even more interested in buying. Almost half of those interested in buying a car can imagine buying such a vehicle within the next five years.
Finally, with respect to the continuously dominant theme of climate change, the survey finds that there is a lot of support for planned policy measures regarding climate change mitigation. Two thirds of the respondents support the climate bonus for commuters who use public transport. A majority of 64 % would like an inexpensive 1-2-3 climate ticket for public transport, and 55 % consider CO 2 tariffs for non-climate-neutral imports to the EU to be sensible. Of particular interest to the Federal Government: an ecological tax reform with fewer taxes on work and fairer taxes on climate-damaging activities is conceivable for the majority surveyed respondents.
Therefore, we made our paper presentation in the form of a video:
In her talk, Hafsa Bousbiat describes how abnormal behavior can be detected among common household devices using Non-Intrusive Load Monitoring. The need for reducing our energy consumption footprint and the increasing number of electric devices in today’s homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household’s occupants for their safety. By breaking down aggregate power consumption into appliance level consumption, Non-Intrusive Load Monitoring allows for reducing the energy consumption footprint and has the potential to indirectly monitor the elderly and help them to fulfil their wish to be more autonomous in a secure manner. Therefore, the work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows us to visualize and process real-time data about the total consumed energy. The proposed architecture is depicted in the figure below.
Proposed architecture for integrating an AAL with an energy monitoring system
More details about our work can be found in the full version of our paper here.
With the development and introduction of smart metering, the energy information from costumers changes from infrequent manual meter readings to fine-grained energy consumption data. On the one hand, these measurements will lead to an improvement in costumers’ energy habits, but on the other hand, the fine-grained data produces information about a household and households’ inhabitants, which give rise to privacy issues because these monitoring results disclose user behavior which could be extracted by smart algorithms and techniques. The loss of privacy by load disaggregation and data mining is a huge upcoming smart grid and social issue which enforces the need for privacy-preserving techniques, which can be divided into the following three possibilities:
Anonymization of metering data: The metering data and customer identity are separated by a third-party id
Privacy-preserving metering data aggregation: Metering data is geographically encapsulated by aggregating the metering data of co-located consumers
Masking and obfuscation of metering data: Masking the power demand by adding or withdrawing the to the meter visible energy demand with the help of rechargeable batteries or controllable loads.
a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented and compared. A load-based load hiding system controls appliances in a specific way to obfuscate a household’s power demand. For example, an electric water boiler could be instrumented to consume energy in a way that masks the power consumption of smaller household devices like coffee machines or a TV. There is no comfort loss expected for the customer: Overall, the boiler will consume a typical amount of energy and produce the expected amount of hot water. Using this approach, however, reduces the predictability of your energy consumption, which is good for privacy, but a disadvantage for grid operators.
Im MONERGY-Projekt wurden ICT Lösungen zur Energieeinsparung in Haushalten erarbeitet. Die Einsparung erfolgt vor allem durch ein “Smart Home”-Konzept, bei dem BewohnerInnen Unterstützung durch Messeinrichtungen, künstliche Intelligenz und visuelles Feedback bekommen. Da bei solchen Konzepten der Mensch Teil der Regelschleife ist, sind die spezifischen Aspekte der Energienutzung in der jeweiligen Region von großer Bedeutung – das heißt es ist vorab notwendig das Verhalten in den jeweiligen Zielregionen, im Projekt Monergy sind das Kärnten und Friaul-Julisch Venetien, zu erfassen. Da es für diese Regionen noch keine detaillierten Daten gab, wurde eine Messkampagne über die Dauer eines Jahres durchgeführt und, auch um anderen ForscherInnen den Einstieg zu erleichtern, die Ergebnisse öffentlich zugänglich gemacht.
Basierend auf diesen Ergebnissen konnten Mechanismen, Algorithmen und Geräte als Basis für ein effektives Energiemanagement entwickelt werden. Im Projekt sind hier die frei verfügbare Mjölnir-Software zum Aufau eines Energiemonitoringsystems, Algorithmen zur Load Disaggregation und ein Konzept für einen selbstorganisierenden Miniaturmarkt der Geräte hervorzuheben.
It is not necessary to stress all the challenges that come allong with wide range integration of renewables into the power grid at this article. You easily find this somewhere else, in this blog or at another source in the web. But I want to focus your attention on a tool that can help to deal with this challenges and work torwards solutions. It is a lot on matching climate dependent production with the personal demand which is currently noticed only in average. The various influences on residential load profiles are diverse and include weather conditions, availability of other resources, personal habits and also social phenomenas. This variaty is a reason why Marija Ilic speaks of just-in-time and just-in-place services in the conntext of Smart Micro Grids.
Developing of or researching on such services requires a flexible simulation tool with possibly high usability. This is exactly what RAPSim is aiming at. RAPSim was presented in front of interested audience at the ISGT Asia – 2014 conference in Kuala Lumpur in May 2014. Since then the software project is downloadable for free at Sourceforge.net.
Connected Smart Grid Objects
The simulation field is a lattice where the user designs a scenario by placing different “grid objects”, e.g., houses, wind turbines, PV panels, etc. Each of the objects can have a model which does all the inner-object calculations. Algorithms handle grid-wide interactions. User-defined models can extend algorithms for the grid and/or models for the grid objects. RAPSim provides the interface with following functions:
A graphical interface to create the intended scenarios and to control the simulator functions.
Functions to save and load simulation scenarios in a generic xml format.
A time thread that models time of day and day of the year and handles up to minute resolution.
Generation of output files in csv-format. Object parameters can be selected to be written into a file at each time step.
Weather simulation which can be done via stochastic models or simulated by measured data.
Topological grid analysis that collects objects of the same bus in a list and aggregates their parameter values.
Administration of algorithms for grid-wide calculations.
Administration of object specific models that can be easily implemented by the user.
The software is written in Java and provided with the source code so that it can be imported into a Java IDE such as eclipse. The development of the software is still ongoing. Try it out and give us feedback by adding a comment or sending a ticket to helps us to further improve it. Please spread the news about the tool, while we will do the best on our part so that onces RAPSim may become a synonym for rapid simulation in the field of mirco grids.
Once more here the link to the sourceforge page.
Publications on RAPSim (with links to fulltext PDF):
The success of the Smart Grid depends on its ability to collect data from heterogeneous sources such as smart meters and smart appliances, as well as the utilization of this information to forecast energy demand and to provide value-added services to users. In our analysis, we discuss requirements for collecting and integrating household data within smart grid applications. We put forward a potential system architecture and report state- of-the-art technologies that can be deployed towards this vision.
A. Monacchi, D. Egarter, and W. Elmenreich. Integrating households into the smart grid. In Proceedings of the Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES13’13), Berkeley, CA, USA, May 2013.