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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 F1-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.

The proliferation of Internet of Things (IoT) devices and big data has catalyzed the emergence of data markets. Regulatory and technological frameworks such as International Data Spaces (IDS) have been developed to facilitate secure data exchange while integrating security and data sovereignty aspects required by laws and regulations, such as the GDPR and NIS2. Recently, novel attack vectors have taken a toll on many enterprises, causing significant damage despite the deployed security mechanisms. Hence, it is reasonable to assume that the IDS may be just as susceptible. In this paper, we conduct a STRIDE threat analysis on IDS to assess its susceptibility to traditional and emerging cybersecurity threats. Specifically, we evaluate novel threats such as Man-in-the-Middle (MitM) attacks, compromised end-user devices, SIM swapping, and potential backdoors in commonly used open-source software. Our analysis identifies multiple vulnerabilities, particularly at the trust boundary (TB) between users and the IDS system. These include the traditionally troublesome Denial of Service (DoS) attacks, key management weaknesses, and the mentioned novel threats. We discuss the hacking techniques, tools, and associated risks to the IDS framework, followed by targeted mitigation strategies and recommendations. This paper provides a framework for performing a STRIDE-based threat analysis of the IDS. Using the proposed methodology, we identified the most potent threats and suggested solutions, thus contributing to the development of a safer and more resilient data space architecture.

Forecasting or modeling the on-off times of domestic appliances has gained increasing attention in recent years. However, comparing currently published results is difficult due to the many different data-sets and performance measures employed. In this paper, we evaluate the performance of three increasingly sophisticated approaches within a common framework on three data-sets each spanning 2 years. The approaches forecast the future on-off times of the appliances for the next 24 h on an hourly basis, solely based on historic energy consumption data. The appliances investigated are driven by user behavior and consume a significant fraction of the household’s total electrical energy consumption. We find that for all algorithms the average area under curve (AUC) in the receiver operating characteristic (ROC) is in the range between 72% and 73%, i.e. indicating mediocre prediction quality. We conclude that historic consumption data alone is not sufficient for a good quality hourly forecast.

Grid-connected photovoltaic (PV) capacity is increasing and is currently estimated to account for 3.0% of worldwide energy generation. One strategy to balance fluctuating PV power is to incentivize self-consumption by shifting certain loads. The potential improvement in the amount of self-consumption is usually estimated using smart meter and PV production data. Smart meter data are usually available only at sampling frequences far below the Nyquist limit. In this paper we investigate how this insufficient sampling rate affects the estimated self-consumption potential of shiftable household appliances (washing machines, tumble dryers and dishwashers). We base our analyses on measured consumption data from 16 households in the UK and corresponding PV data. We found that the simulated results have a marked dependence on the data sampling rate. The amount of self-consumed energy estimated with data sampled every 10 min was overestimated by 30–40% compared to estimations using data with 1 min sampling rate. We therefore recommend to take this factor into account when making predictions on the impact of appliance load shifting on the rate of self-consumption.

Datasets with measurements of both solar electricity production and domestic electricity consumption separated into the major loads are interesting for research focussing on (i) local optimization of solar energy consumption and (ii) non-intrusive load monitoring. To this end, we publish the iHomeLab RAPT dataset consisting of electrical power traces from five houses in the greater Lucerne region in Switzerland spanning a period from 1.5 up to 3.5 years with a sampling frequency of five minutes. For each house, the electrical energy consumption of the aggregated household and specific appliances such as dishwasher, washing machine, tumble dryer, hot water boiler, or heating pump were metered. Additionally, the data includes electric production data from PV panels for all five houses, and battery power flow measurement data from two houses. Thermal metadata is also provided for the three houses with a heating pump.

More and more household appliances connect to the Internet and exchange data freely. This is the foundation for true smart buildings. However, there is still no uniform communication technology available, which can connect all appliances from all vendors. Protocols differ between manufacturers making interoperability difficult or even impossible. Manufacturers cannot rely on a reference for the implementation and real estate developers and operators are reluctant to commit to a system until it is clear which one will prevail. A similar situation is evident in smart grids and applies equally to the energy supply industry. This fragmentation ultimately leads to missed opportunities in terms of business models which could connect customers with service providers. We present a first draft of an architecture: SINA - Smart Interoperability Architecture. SINA is based on existing decentralized infrastructure, which avoids creating a dependency of the market participants on an overpowering service provider. The core element of the technical solution is an open-source module integrated in the private clouds of the manufacturers, energy suppliers and service providers. The architecture addresses problems of data ownership, privacy and data security avoiding central administrative structures. It manages data access and transfer in a decentralized and distributed system. SINA uses a blockchain and smart contracts to make sure that the pieces of information about which data are accessed, by whom they are accessed, how they are processed, and which monetary transactions take place are immutably stored and made available. This allows providers to offer services to users in a transparent and trustworthy manner. Finally, SINA includes a matchmaking block which helps service providers find potential customers and vice versa. This set of features makes SINA unique.