arXiv subjects

Wilfried Elmenreich

Publications and source records attributed to Wilfried Elmenreich.

At least 19 recordsLinked to original sources

arXiv2023

Crossing Roads of Federated Learning and Smart Grids: Overview, Challenges, and Perspectives

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, Federated Learning (FL) offers a good compromise between privacy preservation and the predictive performance of these models. The current paper presents an overview of FL applications in SGs while discussing their advantages and drawbacks, mainly in load forecasting, electric vehicles, fault diagnoses, load disaggregation and renewable energies. In addition, an analysis of main design trends and possible taxonomies is provided considering data partitioning, the communication topology, and security mechanisms. Towards the end, an overview of main challenges facing this technology and potential future directions is presented.

cs.LG↗

arXiv2021

A review on physical and data-driven based nowcasting methods using sky images

Amongst all the renewable energy resources (RES), solar is the most popular form of energy source and is of particular interest for its widely integration into the power grid. However, due to the intermittent nature of solar source, it is of the greatest significance to forecast solar irradiance to ensure uninterrupted and reliable power supply to serve the energy demand. There are several approaches to perform solar irradiance forecasting, for instance satellite-based methods, sky image-based methods, machine learning-based methods, and numerical weather prediction-based methods. In this paper, we present a review on short-term intra-hour solar prediction techniques known as nowcasting methods using sky images. Along with this, we also report and discuss which sky image features are significant for the nowcasting methods.

cs.CV↗

arXiv2020

Investigating the Performance Gap between Testing on Real and Denoised Aggregates in Non-Intrusive Load Monitoring

Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field. In the field of Non-Intrusive Load Monitoring (NILM), performance evaluation can be conducted on real-world aggregate signals, provided by smart energy meters or artificial superpositions of individual load signals (i.e., denoised aggregates). It has long been suspected that testing on these denoised aggregates provides better evaluation results mainly due to the the fact that the signal is less complex. Complexity in real-world aggregate signals increases with the number of unknown/untracked load. Although this is a known performance reporting problem, an investigation in the actual performance gap between real and denoised testing is still pending. In this paper, we examine the performance gap between testing on real-world and denoised aggregates with the aim of bringing clarity into this matter. Starting with an assessment of noise levels in datasets, we find significant differences in test cases. We give broad insights into our evaluation setup comprising three load disaggregation algorithms, two of them relying on neural network architectures. The results presented in this paper, based on studies covering three scenarios with ascending noise levels, show a strong tendency towards load disaggregation algorithms providing significantly better performance on denoised aggregate signals. A closer look into the outcome of our studies reveals that all appliance types could be subject to this phenomenon. We conclude the paper by discussing aspects that could be causing these considerable gaps between real and denoised testing in NILM.

cs.OH↗

arXiv2020

Augmenting an Assisted Living Lab with 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 a 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 its components, Non-Intrusive Load Monitoring provides information on individual appliances and their current state of operation. Since no additional metering equipment is required, residents are not confronted with intrusion into their familiar environment. Our 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 to visualize and process real-time data about the total energy consumed. As a case study, we describe a series of measurements from common household devices and show how abnormal behavior can be detected.

eess.SP↗

arXiv2020

Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation

Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. The lack of standardisation and consensus on evaluation procedures makes reproducibility and comparability extremely difficult. In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms. We divide discussion on comparability into data aspects, performance metrics, and give a close view on evaluation processes. Detailed information on pre-processing as well as data cleaning methods, the importance of unified performance reporting, and the need for complexity measures in load disaggregation are found to be the most urgent issues in NILM-related research. In addition, our evaluation suggests that datasets should be chosen carefully. We conclude by formulating suggestions for future work to enhance comparability.

eess.SP↗

arXiv2019

On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring

To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households. With an emerging discussion of transferability in Non-Intrusive Load Monitoring (NILM), there is a need for domain-specific metrics to assess the performance of NILM algorithms on new test scenarios being unseen buildings. In this paper, we discuss several metrics to assess the generalisation ability of NILM algorithms. These metrics target different aspects of performance evaluation in NILM and are meant to complement the traditional performance evaluation approach. We demonstrate how our metrics can be utilised to evaluate NILM algorithms by means of two case studies. We conduct our studies on several energy consumption datasets and take into consideration five state-of-the-art as well as four baseline NILM solutions. Finally, we formulate research challenges for future work.

cs.LG↗

arXiv2018

Efficiency of Photovoltaic Systems in Mountainous Areas

Photovoltaic (PV) systems have received much attention in recent years due to their ability of efficiently converting solar power into electricity, which offers important benefits to the environment. PV systems in regions with high solar irradiation can produce a higher output but the temperature affects their performance. This paper presents a study on the effect of cold climate at high altitude on the PV system output. We report a comparative case study, which presents measurement results at two distinct sites, one at a height of 612 meters and another one at a mountain site at a height of 1764 meters. This case study applies the maximum power point tracking (MPPT) technique in order to determine maximum power from the PV panel at different azimuth and altitude angles. We used an Arduino system to measure and display the attributes of the PV system. The measurement results indicate an increased efficiency of 42% for PV systems at higher altitude.

physics.soc-ph↗

arXiv2018

Smart grid modeling and simulation - Comparing GridLAB-D and RAPSim via two Case studies

One of the most important tools for the development of the smart grid is simulation. Therefore, analyzing, designing, modeling, and simulating the smart grid will allow to explore future scenarios and support decision making for the grid's development. In this paper, we compare two open source simulation tools for the smart grid, GridLAB-Distribution (GridLAB-D) and Renewable Alternative Power systems Simulation (RAPSim). The comparison is based on the implementation of two case studies related to a power flow problem and the integration of renewable energy resources to the grid. Results show that even for very simple case studies, specific properties such as weather simulation or load modeling are influencing the results in a way that they are not reproducible with a different simulator.

eess.SY↗

arXiv2016

Assisted Energy Management in Smart Microgrids

Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service interruption is an opposite objective to the one of service availability. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.

cs.AI↗

arXiv2015

Time-triggered smart transducer networks

The time-triggered approach is a well-suited approach for building distributed hard real-time systems. Since many applications of transducer networks have real-time requirements, a time-triggered communication interface for smart transducers is desirable, however such a time-triggered interface must still support features for monitoring, maintenance, plug-and-play, etc. The approach of the OMG Smart Transducer Interface consists of clusters of time-triggered smart transducer nodes that contain special interfaces supporting configuration, diagnostics, and maintenance without affecting the deterministic real-time communication. This paper discusses the applicability of the time-triggered approach for smart transducer networks and presents a case study application of a time-triggered smart transducer network.

cs.NI↗

arXiv2015

An Open Solution to Provide Personalized Feedback for Building Energy Management

The integration of renewable energy sources increases the complexity in mantaining the power grid. In particular, the highly dynamic nature of generation and consumption demands for a better utilization of energy resources, which seen the cost of storage infrastructure, can only be achieved through demand-response. Accordingly, the availability of energy and potential overload situations can be reflected using a price signal. The effectiveness of this mechanism arises from the flexibility of device operation, which is nevertheless heavily reliant on the exchange of information between the grid and its consumers. In this paper, we investigate the capability of an interactive energy management system to timely inform users on energy usage, in order to promote an optimal use of local resources. In particular, we analyze data being collected in several households in Italy and Austria to gain insights into usage behavior and drive the design of more effective systems. The outcome is the formulation of energy efficiency policies for residential buildings, as well as the design of an energy management system, consisting of hardware measurement units and a management software. The Mjölnir framework, which we release for open use, provides a platform where various feedback concepts can be implemented and assessed. This includes widgets displaying disaggregated and aggregated consumption information, as well as daily production and tailored advices. The formulated policies were implemented as an advisor widget able to autonomously analyze usage and provide tailored energy feedback.

cs.HC↗

arXiv2015

Proficiency of Power Values for Load Disaggregation

Load disaggregation techniques infer the operation of different power consuming devices from a single measurement point that records the total power draw over time. Thus, a device consuming power at the moment can be understood as information encoded in the power draw. However, similar power draws or similar combinations of power draws limit the ability to detect the currently active device set. We present an information coding perspective of load disaggregation to enable a better understanding of this process and to support its future improvement. In typical cases of quantity and type of devices and their respective power consumption, not all possible device configurations can be mapped to distinguishable power values. We introduce the term of proficiency to describe the suitability of a device set for load disaggregation. We provide the notion and calculation of entropy of initial device states, mutual information of power values and the resulting uncertainty coefficient or proficiency. We show that the proficiency is highly dependent from the device running probability especially for devices with multiple states of power consumption. The application of the concept is demonstrated by exemplary artificial data as well as with actual power consumption data from real-world power draw datasets.

cs.IT↗

arXiv2015

Complexity of Power Draws for Load Disaggregation

Non-Intrusive Load Monitoring (NILM) is a technology offering methods to identify appliances in homes based on their consumption characteristics and the total household demand. Recently, many different novel NILM approaches were introduced, tested on real-world data and evaluated with a common evaluation metric. However, the fair comparison between different NILM approaches even with the usage of the same evaluation metric is nearly impossible due to incomplete or missing problem definitions. Each NILM approach typically is evaluated under different test scenarios. Test results are thus influenced by the considered appliances, the number of used appliances, the device type representing the appliance and the pre-processing stages denoising the consumption data. This paper introduces a novel complexity measure of aggregated consumption data providing an assessment of the problem complexity affected by the used appliances, the appliance characteristics and the appliance usage over time. We test our load disaggregation complexity on different real-world datasets and with a state-of-the-art NILM approach. The introduced disaggregation complexity measure is able to classify the disaggregation problem based on the used appliance set and the considered measurement noise.

cs.OH↗

arXiv2014

Autonomous Load Disaggregation Approach based on Active Power Measurements

With the help of smart metering valuable information of the appliance usage can be retrieved. In detail, non-intrusive load monitoring (NILM), also called load disaggregation, tries to identify appliances in the power draw of an household. In this paper an unsupervised load disaggregation approach is proposed that works without a priori knowledge about appliances. The proposed algorithm works autonomously in real time. The number of used appliances and the corresponding appliance models are learned in operation and are progressively updated. The proposed algorithm is considering each useful and suitable detected power state. The algorithm tries to detect power states corresponding to on/off appliances as well as to multi-state appliances based on active power measurements in 1s resolution. We evaluated the novel introduced load disaggregation approach on real world data by testing the possibility to disaggregate energy demand on appliance level.

cs.OH↗

arXiv2014

YoMo - The Arduino based Smart Metering Board

Smart meters are an enabling technology for many smart grid applications. This paper introduces a design for a low-cost smart meter system as well as the fundamentals of smart metering. The smart meter platform, provided as open hardware, is designed with a connector interface compatible to the Arduino platform, thus opening the possibilities for smart meters with flexible hardware and computation features, starting from low-cost 8 bit micro controllers up to powerful single board computers that can run Linux. The metering platform features a current transformer which allows a non-intrusive installation of the current measurement unit. The suggested design can switch loads, offers a variable sampling frequency, and provides measurement data such as active power, reactive and apparent power. Results indicate that measurement accuracy and resolution of the proposed metering platform are sufficient for a range of different applications and loads from a few watts up to five kilowatts.

cs.OH↗

arXiv2014

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

cs.OH↗

arXiv2014

Load Hiding of Household's Power Demand

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers' energy habits, but on the other hand the fined-grained data produces information about a household and also households' inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, 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. An LLH system uses an controllable household appliance to obfuscate the household's power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.

cs.OH↗

arXiv2014

GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we benchmark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

cs.OH↗