SYND - A Synthetic Energy Dataset

As with related Machine Learning problems, applications like Non-Intrusive Load Monitoring (NILM) require a sufficient amount of data to train and validate new approaches. With SynD, we present a synthetic energy dataset emulating the power consumption of residential buildings. The dataset is freely available and contains 180 days of synthetic power data on aggregate level and individual appliances.

The SynD dataset is based upon
measurements of real devices

SynD is the result of a custom simulation process that relies on power traces of real household appliances. During a measurement campaign in two Austrian households, we monitored 21 electrical household appliances. The main goal of the measurement campaign was to record representative power consumption patterns of those 21 appliances, where a each pattern is represented by the shape of the power consumption over time for a single operation.

Technical validation of SYND by comparing with other datasets

In contrast to datasets entirely based on measurement campaigns, such as our dataset GREEND, the SynD dataset is constructed from a simulation model utilizing the measured devices. This way, a synthetic but realistic power consumption dataset can be obtained. In a technical validation of the dataset we compared SynD with a number of measured datasets showing that SynD is well within the varaiation between mutual datasets.

Wilfried Elmenreich states “Usually I rely on measured data, but with the SYND dataset, we are among the first who created a convincing synthetic dataset.”

The full paper describing the dataset is available under an open access policy here:

Christoph Klemenjak, Christoph Kovatsch, Manuel Herold, and Wilfried Elmenreich. A synthetic energy dataset for non-intrusive load monitoring in households. Scientific Data, 7(1):1–17, 2020. (doi:10.6084/m9.figshare.11940324)

The SynD dataset can be obtained freely at the SynD Github Repository.

Supporting the Grid with Dynamic Residential Load Scheduling

Demand response (DR) for smart grids intends to balance the required power demand with the available supply resources. This is especially important with an increased amount of renewable energy sources since for most of them the energy yield cannot be shifted in time. For example, photovoltaic systems will provide their peak power at noon while customers might want to use energy at different times.

Residential load scheduling systems provide a solution to this problem by incentivizing consumers to use energy at times where production is high while motivating lower energy consumption in times of peak power. The goals of such residential load scheduling systems are therefore manifold: to cut peak power, to follow supply and to reduce the overall energy cost for the customer.

In a study we investigated different dynamic residential load scheduling systems with respect to optimal scheduling of household appliances on the basis of an adaptive consumption level pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance of the proposed DR pricing scheme to achieve lower energy bills.

Dynamic pricing scheme motivating customers
to avoid energy consumption in peak periods

Simulation results show that employing the proposed approach benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by  35%.

The full results are available in the paper

H. T. Haider, O. H. See, and W. Elmenreich. Dynamic residential load scheduling based on adaptive consumption level pricing scheme. Electric Power Systems Research, 133:27–35, 2016.

Research Scholarship in the Field of Energy Management and Technology

The scholarship will support a scientific project to be done at the Alpen-Adria-Universität Klagenfurt (for example as part of a dissertation).

Applicants must

• have a completed master or diploma degree
• Austrian citizenship or analogous according to § 4 StudFG (EU citizenship)
• the average monthly additional income during the scholarship may not exceed the amount of € 679, - (net)
• not have at an active employment contract with Alpen-Adria-Universität Klagenfurt during the scholarship
• target a scientific project within the topics of the research cluster energy management and technology (e.g., smart grids) to be done at the Alpen-Adria-Universität Klagenfurt

How to apply

submit a single PDF document no later than August 16 2015 to wilfried.elmenreich@aau.at with

• project description (English, max. 3 pages)
• research methodology
• time plan
• CV publication list
• Support letter from supervisor
• Leaving certificate of the respective field of study (eg Master certificate)
• Proof of citizenship
• Affidavit that the specified income level is not exceeded

For detailed information, visit https://energy.aau.at/