Energies

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3 pages, 158 KiB

Open AccessEditorial

Special Issue: “Energy Data Analytics for Smart Meter Data”

by Andreas Reinhardt and Lucas Pereira

Energies 2021, 14(17), 5376; https://doi.org/10.3390/en14175376 - 30 Aug 2021

Cited by 4 | Viewed by 2179

Abstract

Smart electricity meters are a cornerstone for the realization of next-generation electrical power grids [...] Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

Research

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18 pages, 4369 KiB

Open AccessArticle

Satisfaction-Based Energy Allocation with Energy Constraint Applying Cooperative Game Theory

by Samira Ortiz, Mandoye Ndoye and Marcel Castro-Sitiriche

Energies 2021, 14(5), 1485; https://doi.org/10.3390/en14051485 - 9 Mar 2021

Cited by 5 | Viewed by 2693

Abstract

There has been an effort for a few decades to keep energy consumption at a minimum or at least within a low-level range. This effort is more meaningful and complex by including a customer’s satisfaction variable to ensure that customers can achieve the [...] Read more.

There has been an effort for a few decades to keep energy consumption at a minimum or at least within a low-level range. This effort is more meaningful and complex by including a customer’s satisfaction variable to ensure that customers can achieve the best quality of life that could be derived from how energy is used by different devices. We use the concept of Shapley Value from cooperative game theory to solve the multi-objective optimization problem (MOO) to responsibly fulfill user’s satisfaction by maximizing satisfaction while minimizing the power consumption, with energy constrains since highly limited resources scenarios are studied. The novel method uses the concept of a quantifiable user satisfaction, along the concepts of power satisfaction (PS) and energy satisfaction (ES). The model is being validated by representing a single house (with a small PV system) that is connected to the utility grid. The main objectives are to (i) present the innovative energy satisfaction model based on responsible wellbeing, (ii) demonstrate its implementation using a Shapley-value-based algorithm, and (iii) include the impact of a solar photovoltaic (PV) system in the energy satisfaction model. The proposed technique determines in which hours the energy should be allocated to maximize the ES for each scenario, and then it is compared to cases in which devices are usually operated. Through the proposed technique, the energy consumption was reduced 75% and the ES increased 40% under the energy constraints. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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16 pages, 600 KiB

Open AccessArticle

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

by Veronica Piccialli and Antonio M. Sudoso

Energies 2021, 14(4), 847; https://doi.org/10.3390/en14040847 - 5 Feb 2021

Cited by 74 | Viewed by 6217

Abstract

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose [...] Read more.

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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23 pages, 1023 KiB

Open AccessArticle

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

by Christos Athanasiadis, Dimitrios Doukas, Theofilos Papadopoulos and Antonios Chrysopoulos

Energies 2021, 14(3), 767; https://doi.org/10.3390/en14030767 - 1 Feb 2021

Cited by 80 | Viewed by 5801

Abstract

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an [...] Read more.

Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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26 pages, 14686 KiB

Open AccessArticle

Identification of the State of Electrical Appliances with the Use of a Pulse Signal Generator

by Augustyn Wójcik, Piotr Bilski, Robert Łukaszewski, Krzysztof Dowalla and Ryszard Kowalik

Energies 2021, 14(3), 673; https://doi.org/10.3390/en14030673 - 28 Jan 2021

Cited by 4 | Viewed by 2265

Abstract

The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the [...] Read more.

The paper presents the novel HF-GEN method for determining the characteristics of Electrical Appliance (EA) operating in the end-user environment. The method includes a measurement system that uses a pulse signal generator to improve the quality of EA identification. Its structure and the principles of operation are presented. A method for determining the characteristics of the current signals’ transients using the cross-correlation is described. Its result is the appliance signature with a set of features characterizing its state of operation. The quality of the obtained signature is evaluated in the standard classification task with the aim of identifying the particular appliance’s state based on the analysis of features by three independent algorithms. Experimental results for 15 EAs categories show the usefulness of the proposed approach. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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23 pages, 4475 KiB

Open AccessFeature PaperArticle

The Impact of Ambient Sensing on the Recognition of Electrical Appliances

by Jana Huchtkoetter, Marcel Alwin Tepe and Andreas Reinhardt

Energies 2021, 14(1), 188; https://doi.org/10.3390/en14010188 - 1 Jan 2021

Cited by 3 | Viewed by 2402

Abstract

Smart spaces are characterized by their ability to capture a holistic picture of their contextual situation. This often includes the detection of the operative states of electrical appliances, which in turn allows for the recognition of user activities and intentions. For electrical appliances [...] Read more.

Smart spaces are characterized by their ability to capture a holistic picture of their contextual situation. This often includes the detection of the operative states of electrical appliances, which in turn allows for the recognition of user activities and intentions. For electrical appliances with largely different power consumption characteristics, their types and operational times can be easily inferred from data collected at a single metering point (typically, a smart meter). However, a disambiguation between consumers of the same type and model, yet located in different areas of a smart building, is not possible this way. Likewise, small consumers (e.g., wall chargers) are often indiscernible from measurement noise and spurious power consumption events of other appliances. As a consequence thereof, we investigate how additional sensing modalities, i.e., data beyond electrical signals, can be leveraged to improve the appliance detection accuracy. Through a set of practical experiments, recording ambient influences in eight dimensions and testing their effects on 21 appliance types, we evaluate the importance of such added features in the context of appliance recognition. Our results show that electrical power measurements already yield a high appliance recognition accuracy, yet further accuracy improvements are possible when considering ambient parameters as well. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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26 pages, 9709 KiB

Open AccessFeature PaperArticle

A Framework to Generate and Label Datasets for Non-Intrusive Load Monitoring

by Benjamin Völker, Marc Pfeifer, Philipp M. Scholl and Bernd Becker

Energies 2021, 14(1), 75; https://doi.org/10.3390/en14010075 - 25 Dec 2020

Cited by 3 | Viewed by 3862

Abstract

In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide [...] Read more.

In order to reduce the electricity consumption in our homes, a first step is to make the user aware of it. Raising such awareness, however, demands to pinpoint users of specific appliances that unnecessarily consume electricity. A retrofittable and scalable way to provide appliance-specific consumption is provided by Non-Intrusive Load Monitoring methods. These methods use a single electricity meter to record the aggregated consumption of all appliances and disaggregate it into the consumption of each individual appliance using advanced algorithms usually utilizing machine-learning approaches. Since these approaches are often supervised, labelled ground-truth data need to be collected in advance. Labeling on-phases of devices is already a tedious process, but, if further information about internal device states is required (e.g., intensity of an HVAC), manual post-processing quickly becomes infeasible. We propose a novel data collection and labeling framework for Non-Intrusive Load Monitoring. The framework is comprised of the hardware and software required to record and (semi-automatically) label the data. The hardware setup includes a smart-meter device to record aggregated consumption data and multiple socket meters to record appliance level data. Labeling is performed in a semi-automatic post-processing step guided by a graphical user interface, which reduced the labeling effort by 72% compared to a manual approach. We evaluated our framework and present the FIRED dataset. The dataset features uninterrupted, time synced aggregated, and individual device voltage and current waveforms with distinct state transition labels for a total of 101 days. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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17 pages, 2724 KiB

Open AccessArticle

A Novel Electricity Theft Detection Scheme Based on Text Convolutional Neural Networks

by Xiaofeng Feng, Hengyu Hui, Ziyang Liang, Wenchong Guo, Huakun Que, Haoyang Feng, Yu Yao, Chengjin Ye and Yi Ding

Energies 2020, 13(21), 5758; https://doi.org/10.3390/en13215758 - 3 Nov 2020

Cited by 26 | Viewed by 3159

Abstract

Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily [...] Read more.

Electricity theft decreases electricity revenues and brings risks to power usage’s safety, which has been increasingly challenging nowadays. As the mainstream in the relevant studies, the state-of-the-art data-driven approaches mainly detect electricity theft events from the perspective of the correlations between different daily or weekly loads, which is relatively inadequate to extract features from hours or more of fine-grained temporal data. In view of the above deficiencies, we propose a novel electricity theft detection scheme based on text convolutional neural networks (TextCNN). Specifically, we convert electricity consumption measurements over a horizon of interest into a two-dimensional time-series containing the intraday electricity features. Based on the data structure, the proposed method can accurately capture various periodical features of electricity consumption. Moreover, a data augmentation method is proposed to cope with the imbalance of electricity theft data. Extensive experimental results based on realistic Chinese and Irish datasets indicate that the proposed model achieves a better performance compared with other existing methods. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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35 pages, 3970 KiB

Open AccessArticle

A Dataset for Non-Intrusive Load Monitoring: Design and Implementation

by Douglas Paulo Bertrand Renaux, Fabiana Pottker, Hellen Cristina Ancelmo, André Eugenio Lazzaretti, Carlos Raiumundo Erig Lima, Robson Ribeiro Linhares, Elder Oroski, Lucas da Silva Nolasco, Lucas Tokarski Lima, Bruna Machado Mulinari, José Reinaldo Lopes da Silva, Júlio Shigeaki Omori and Rodrigo Braun dos Santos

Energies 2020, 13(20), 5371; https://doi.org/10.3390/en13205371 - 15 Oct 2020

Cited by 28 | Viewed by 6386

Abstract

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such [...] Read more.

A NILM dataset is a valuable tool in the development of Non-Intrusive Load Monitoring techniques, as it provides a means of evaluation of novel techniques and algorithms, as well as for benchmarking. The figure of merit of a NILM dataset includes characteristics such as the sampling frequency of the voltage, current, or power, the availability of indications (ground-truth) of load events during recording, the variety and representativeness of the loads, and the variety of situations these loads are subject to. Considering such aspects, the proposed LIT-Dataset was designed, populated, evaluated, and made publicly available to support NILM development. Among the distinct features of the LIT-Dataset is the labeling of the load events at sample level resolution and with an accuracy and precision better than 5 ms. The availability of such precise timing information, which also includes the identification of the load and the sort of power event, is an essential requirement both for the evaluation of NILM algorithms and techniques, as well as for the training of NILM systems, particularly those based on Machine Learning. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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18 pages, 2348 KiB

Open AccessArticle

Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

by Manu Lahariya, Dries F. Benoit and Chris Develder

Energies 2020, 13(16), 4211; https://doi.org/10.3390/en13164211 - 14 Aug 2020

Cited by 31 | Viewed by 5903

Abstract

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze [...] Read more.

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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17 pages, 1059 KiB

Open AccessArticle

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

by Anthony Faustine and Lucas Pereira

Energies 2020, 13(16), 4154; https://doi.org/10.3390/en13164154 - 11 Aug 2020

Cited by 42 | Viewed by 5320

Abstract

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads [...] Read more.

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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30 pages, 6865 KiB

Open AccessArticle

Privacy-Functionality Trade-Off: A Privacy-Preserving Multi-Channel Smart Metering System

by Xiao-Yu Zhang, Stefanie Kuenzel, José-Rodrigo Córdoba-Pachón and Chris Watkins

Energies 2020, 13(12), 3221; https://doi.org/10.3390/en13123221 - 21 Jun 2020

Cited by 16 | Viewed by 4402

Abstract

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the [...] Read more.

While smart meters can provide households with more autonomy regarding their energy consumption, they can also be a significant intrusion into the household’s privacy. There is abundant research implementing protection methods for different aspects (e.g., noise-adding and data aggregation, data down-sampling); while the private data are protected as sensitive information is hidden, some of the compulsory functions such as Time-of-use (TOU) billing or value-added services are sacrificed. Moreover, some methods, such as rechargeable batteries and homomorphic encryption, require an expensive energy storage system or central processor with high computation ability, which is unrealistic for mass roll-out. In this paper, we propose a privacy-preserving smart metering system which is a combination of existing data aggregation and data down-sampling mechanisms. The system takes an angle based on the ethical concerns about privacy and it implements a hybrid privacy-utility trade-off strategy, without sacrificing functionality. In the proposed system, the smart meter plays the role of assistant processor rather than information sender/receiver, and it enables three communication channels to transmit different temporal resolution data to protect privacy and allow freedom of choice: high frequency feed-level/substation-level data are adopted for grid operation and management purposes, low frequency household-level data are used for billing, and a privacy-preserving valued-add service channel to provide third party (TP) services. In the end of the paper, the privacy performance is evaluated to examine whether the proposed system satisfies the privacy and functionality requirements. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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20 pages, 3285 KiB

Open AccessArticle

Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier

by Zhengwei Qu, Hongwen Li, Yunjing Wang, Jiaxi Zhang, Ahmed Abu-Siada and Yunxiao Yao

Energies 2020, 13(8), 2039; https://doi.org/10.3390/en13082039 - 19 Apr 2020

Cited by 55 | Viewed by 4334

Abstract

Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data on the users’ side. To improve the auditing efficiency of [...] Read more.

Effective detection of electricity theft is essential to maintain power system reliability. With the development of smart grids, traditional electricity theft detection technologies have become ineffective to deal with the increasingly complex data on the users’ side. To improve the auditing efficiency of grid enterprises, a new electricity theft detection method based on improved synthetic minority oversampling technique (SMOTE) and improve random forest (RF) method is proposed in this paper. The data of normal and electricity theft users were classified as positive data (PD) and negative data (ND), respectively. In practice, the number of ND was far less than PD, which made the dataset composed of these two types of data become unbalanced. An improved SOMTE based on K-means clustering algorithm (K-SMOTE) was firstly presented to balance the dataset. The cluster center of ND was determined by K-means method. Then, the ND were interpolated by SMOTE on the basis of the cluster center to balance the entire data. Finally, the RF classifier was trained with the balanced dataset, and the optimal number of decision trees in RF was decided according to the convergence of out-of-bag data error (OOB error). Electricity theft behaviors on the user side were detected by the trained RF classifier. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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Review

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34 pages, 926 KiB

Open AccessReview

Review on Deep Neural Networks Applied to Low-Frequency NILM

by Patrick Huber, Alberto Calatroni, Andreas Rumsch and Andrew Paice

Energies 2021, 14(9), 2390; https://doi.org/10.3390/en14092390 - 23 Apr 2021

Cited by 88 | Viewed by 9249

Abstract

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview [...] Read more.

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F

_{1}

-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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21 pages, 419 KiB

Open AccessReview

Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

by Benjamin Völker, Andreas Reinhardt, Anthony Faustine and Lucas Pereira

Energies 2021, 14(3), 719; https://doi.org/10.3390/en14030719 - 30 Jan 2021

Cited by 44 | Viewed by 7158

Abstract

The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the [...] Read more.

The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters. Full article

(This article belongs to the Special Issue Energy Data Analytics for Smart Meter Data)

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