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This paper compares two different deep-learning architectures for the use in energy disaggregation and Non-Intrusive Load Monitoring. Non-Intrusive Load Monitoring breaks down the aggregated energy consumption into individual appliance consumptions, thus detecting device operation. In detail, the “One versus All” approach, where one deep neural network per appliance is trained, and the “Multi-Output” approach, where the number of output nodes is equal to the number of appliances, are compared to each other. Evaluation is done on a state-of-the-art baseline system using standard performance measures and a set of publicly available datasets out of the REDD database.
Pascal A. Schirmer; Iosif Mporas. Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation. Springer Proceedings in Energy 2021, 45 -51.
AMA StylePascal A. Schirmer, Iosif Mporas. Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation. Springer Proceedings in Energy. 2021; ():45-51.
Chicago/Turabian StylePascal A. Schirmer; Iosif Mporas. 2021. "Binary versus Multiclass Deep Learning Modelling in Energy Disaggregation." Springer Proceedings in Energy , no. : 45-51.
Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.
Pascal Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. Energies 2021, 14, 2485 .
AMA StylePascal Schirmer, Iosif Mporas, Akbar Sheikh-Akbari. Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation. Energies. 2021; 14 (9):2485.
Chicago/Turabian StylePascal Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. 2021. "Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation." Energies 14, no. 9: 2485.
Energy storage systems will play a key role in the establishment of future smart grids. Specifically, the integration of storages into the grid architecture serves several purposes, including the handling of the statistical variation of energy supply through increasing usage of renewable energy sources as well as the optimization of the daily energy usage through load scheduling. This article is focusing on the reduction of the grid distortions using non-linear convex optimization. In detail an analytic storage model is used in combination with a load forecasting technique based on socio-economic information of a community of households. It is shown that the proposed load forecasting technique leads to significantly reduced forecasting errors (relative reductions up-to 14.2%), while the proposed storage optimization based on non-linear convex optimizations leads to 12.9% reductions in terms of peak to average values for ideal storages and 9.9% for storages with consideration of losses respectively. Furthermore, it was shown that the largest improvements can be made when storages are utilized for a community of households with a storage size of 4.6-8.2 kWh per household showing the effectiveness of shared storages as well as load forecasting for a community of households.
Pascal A. Schirmer; Christian Geiger; Iosif Mporas. Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages. IEEE Access 2021, 9, 15122 -15132.
AMA StylePascal A. Schirmer, Christian Geiger, Iosif Mporas. Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages. IEEE Access. 2021; 9 ():15122-15132.
Chicago/Turabian StylePascal A. Schirmer; Christian Geiger; Iosif Mporas. 2021. "Reducing Grid Distortions Utilizing Energy Demand Prediction and Local Storages." IEEE Access 9, no. : 15122-15132.
Energy smart meters have become very popular in monitoring and smart energy management applications. However, the acquired measurements except the energy consumption information may also carry information about the residents’ daily routine, preferences and profile. In this article, we investigate the potential of extracting information from smart meters related to residents’ security- and privacy-sensitive information. Specifically, using methodologies for load demand prediction, non-intrusive load monitoring and elastic matching, evaluation of extraction of information related to house occupancy, multimedia watching detection, socioeconomic and health profiling of residents was performed. The evaluation results showed that the aggregated energy consumption signals contain information related to residents’ privacy and security, which can be extracted from the smart meter measurements.
Pascal Alexander Schirmer; Iosif Mporas. On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters. Neural Computing and Applications 2021, 1 -14.
AMA StylePascal Alexander Schirmer, Iosif Mporas. On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters. Neural Computing and Applications. 2021; ():1-14.
Chicago/Turabian StylePascal Alexander Schirmer; Iosif Mporas. 2021. "On the non-intrusive extraction of residents’ privacy- and security-sensitive information from energy smart meters." Neural Computing and Applications , no. : 1-14.
A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.
Pascal A. Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. Energies 2020, 13, 2148 .
AMA StylePascal A. Schirmer, Iosif Mporas, Akbar Sheikh-Akbari. Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors. Energies. 2020; 13 (9):2148.
Chicago/Turabian StylePascal A. Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. 2020. "Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors." Energies 13, no. 9: 2148.
An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.
Pascal Alexander Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. Robust energy disaggregation using appliance-specific temporal contextual information. EURASIP Journal on Advances in Signal Processing 2020, 2020, 1 -13.
AMA StylePascal Alexander Schirmer, Iosif Mporas, Akbar Sheikh-Akbari. Robust energy disaggregation using appliance-specific temporal contextual information. EURASIP Journal on Advances in Signal Processing. 2020; 2020 (1):1-13.
Chicago/Turabian StylePascal Alexander Schirmer; Iosif Mporas; Akbar Sheikh-Akbari. 2020. "Robust energy disaggregation using appliance-specific temporal contextual information." EURASIP Journal on Advances in Signal Processing 2020, no. 1: 1-13.
In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.
Pascal A. Schirmer; Iosif Mporas; Michael Paraskevas. Energy Disaggregation Using Elastic Matching Algorithms. Entropy 2020, 22, 71 .
AMA StylePascal A. Schirmer, Iosif Mporas, Michael Paraskevas. Energy Disaggregation Using Elastic Matching Algorithms. Entropy. 2020; 22 (1):71.
Chicago/Turabian StylePascal A. Schirmer; Iosif Mporas; Michael Paraskevas. 2020. "Energy Disaggregation Using Elastic Matching Algorithms." Entropy 22, no. 1: 71.
In this paper we evaluate several well-known and widely used machine learning algorithms for regression in the energy disaggregation task. Specifically, the Non-Intrusive Load Monitoring approach was considered and the K-Nearest-Neighbours, Support Vector Machines, Deep Neural Networks and Random Forest algorithms were evaluated across five datasets using seven different sets of statistical and electrical features. The experimental results demonstrated the importance of selecting both appropriate features and regression algorithms. Analysis on device level showed that linear devices can be disaggregated using statistical features, while for non-linear devices the use of electrical features significantly improves the disaggregation accuracy, as non-linear appliances have non-sinusoidal current draw and thus cannot be well parametrized only by their active power consumption. The best performance in terms of energy disaggregation accuracy was achieved by the Random Forest regression algorithm.
Pascal Schirmer; Iosif Mporas. Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation. Sustainability 2019, 11, 3222 .
AMA StylePascal Schirmer, Iosif Mporas. Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation. Sustainability. 2019; 11 (11):3222.
Chicago/Turabian StylePascal Schirmer; Iosif Mporas. 2019. "Statistical and Electrical Features Evaluation for Electrical Appliances Energy Disaggregation." Sustainability 11, no. 11: 3222.