Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.

The advances in the Internet of Things (IoT) and cloud computing opened new opportunities for developing various smart grid applications and services. The rapidly increasing adoption of IoT devices has enabled the development of applications and solutions to manage energy consumption efficiently. This work presents the design and implementation of a home energy management system (HEMS), which allows collecting and storing energy consumption data from appliances and the main load of the home. Two scenarios are designed and implemented: a local HEMS isolated from the Internet and relies on its processing and storage duties using an edge device and a Cloud HEMS using AWS IoT Core to manage incoming data messages and provide data-driven services and applications. A testbed was carried out in a real house in the city of Valparaiso, Chile, over a one-year period, where four appliances were used to collect energy consumption using smart plugs, as well as collecting the main energy load of the house through a data logger acting as a smart meter. To the best of our knowledge, this is the first electrical energy dataset with a 10-second sampling rate from a real household in Valparaiso, Chile. Results show that both implementations perform the baseline tasks (collecting, storing, and controlling) for a HEMS. This work contributes by providing a detailed technical implementation of HEMS that enables researchers and engineers to develop and implement HEMS solutions to support different smart home applications.

This paper proposes a blockchain-based energy trading platform for electric vehicles in smart campus parking lots. Smart parking lots are smart places capable of supporting both parking and charging services for electric vehicles. The electric vehicle owner may want to charge energy at a low price and sell it during peak hours at a higher price. The proposed system architecture consists of two layers: the physical infrastructure layer and the cyber infrastructure layer. The physical infrastructure layer represents all of the physical components located in the campus distribution power system, such as electric vehicles charging stations, transformers, and electric feeders, while the cyber infrastructure layer supports the operation of the physical infrastructure layer and enables selling/buying energy among participants. Blockchain technology is a promising candidate to facilitate auditability and traceability of energy transactions among participants. A real case of a parking lot with a realistic parking pattern in a university campus is considered. The system consists of a university control center and various parking lot local controllers (PLLCs). The PLLC broadcasts the electricity demand and the grid price, and each electric vehicle owner decides whether to charge/discharge based on their benefits. The proposed system is implemented on Hyperledger Fabric. Participants, assets, transactions, and smart contracts are defined and discussed. Two scenarios are considered. The first scenario represents energy trading between electric vehicles as sellers and the PLLC as a buyer, while the second scenario involves energy trading between electric vehicles as buyers and the PLLC as a seller. The proposed platform provides profits for participants, as well as enables balancing for the university load demand locally.

The widespread adoption of distributed energy resources (DERs) and the progress made in internet of things (IoT) and cloud computing technologies have enabled and facilitated the development of various smart grid applications and services. This study aims to develop and implement a peer-to-peer (P2P) energy trading platform that allows local energy trading between consumers and prosumers within a microgrid which combines IoT and blockchain technologies. The proposed platform comprises an IoT-cloud home energy management system (HEMS) responsible for gathering and storing energy consumption data and incorporates a blockchain framework that ensures secure and transparent energy trading. The proposed IoT–blockchain architecture utilizes a Chainlink oracle network and a private Ethereum blockchain. Through the use of smart contracts, consumers and prosumers can participate in an open auction to trade energy, while the settlement process involves acquiring external energy data from an API through the oracle network. The performance of the platform is evaluated through a testbed scenario using real-world energy data from a real house in Valparaiso, Chile, while storing those measurements in AWS cloud, validating the feasibility of the proposed architecture in enabling local energy trading. This work contributes to the development of energy management systems by providing a real-world implementation of an IoT–blockchain architecture for local energy trading. The integration of these technologies will allow for a more efficient and secure energy trading system that can benefit prosumers, consumers, and utilities.

Internet of Things (IoT) technologies will play an important role in enabling the smart grid achieving its goals in monitoring, protecting, and controlling by incorporating sensors, actuators, and metering devices while supporting various network functions and system automation. In this regard, home energy management systems (HEMS) enable customers efficiently use energy by managing their consumption, providing feedback information and improving control of major appliances. This work proposes a novel framework for IoT based appliance recognition in smart homes. It consists of two parts: training framework and inference framework. The proposed framework allows incorporating different loads in the monitoring system and enables selecting and testing specific parameters related to dataset configuration, feature extraction, and classifier model setting. The work contributes by developing an easy-to-use tool that allows customization of the training/prediction parameters according to the user criterion. Once the data and all its parameters are loaded, a novel feature extraction algorithm is used to obtain a total of ten statistical features. For the classification task, three machine learning models are included: a feed-forward neural network, a long short-term memory and a support vector machine. In addition, the user can apply a set of techniques to handle imbalanced classes, and also measure the influence of the selected features in the classifiers’ prediction by performing a feature importance analysis.

In this work, we an envision Home Energy Management System (HEMS) as a Cyber-Physical System (CPS) architecture including three stages: Data Acquisition, Communication Network, and Data Analytics. In this CPS, monitoring, forecasting, comfort, occupation, and other strategies are conceived to feed a control plane representing the decision-making process. We survey the main technologies and techniques implemented in the recent years for each of the stages, reviewing and identifying the cutting-edge challenges that the research community are currently facing. For the Acquisition part, we define a metering device according to the IEC TS 63297:2021 Standard. We analyze the communication infrastructure as part of beyond 2030 communication era (5G and 6G), and discuss the Analytics stage as the cyber part of the CPS-based HEMS. To conclude, we present a case study in which, using real data collected in an experimental environment, we validate proposed architecture of HEMS in monitoring tasks. Results revealed an accuracy of 99.2% in appliance recognition compared with the state-of-the-art proposals.