NILM techniques for intelligent home energy management and ambient assisted living: a review

The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.Agência financiadora: Programa Operacional Portugal 2020 and Programa Operacional Regional do Algarve 01/SAICT/2018/39578 Fundação para a Ciência e Tecnologia through IDMEC, under LAETA: SFRH/BSAB/142998/2018 SFRH/BSAB/142997/2018 UID/EMS/50022/2019 Junta de Comunidades de Castilla-La-Mancha, Spain: SBPLY/17/180501/000392 Spanish Ministry of Economy, Industry and Competitiveness (SOC-PLC project): TEC2015-64835-C3-2-R MINECO/FEDERinfo:eu-repo/semantics/publishedVersio

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

Optimized Household Demand Management with Local Solar PV Generation

Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid

Demand Response Strategy Based on Reinforcement Learning and Fuzzy Reasoning for Home Energy Management

As energy demand continues to increase, demand response (DR) programs in the electricity distribution grid are gaining momentum and their adoption is set to grow gradually over the years ahead. Demand response schemes seek to incentivise consumers to use green energy and reduce their electricity usage during peak periods which helps support grid balancing of supply-demand and generate revenue by selling surplus of energy back to the grid. This paper proposes an effective energy management system for residential demand response using Reinforcement Learning (RL) and Fuzzy Reasoning (FR). RL is considered as a model-free control strategy which learns from the interaction with its environment by performing actions and evaluating the results. The proposed algorithm considers human preference by directly integrating user feedback into its control logic using fuzzy reasoning as reward functions. Q-learning, a RL strategy based on a reward mechanism, is used to make optimal decisions to schedule the operation of smart home appliances by shifting controllable appliances from peak periods, when electricity prices are high, to off-peak hours, when electricity prices are lower without affecting the customer’s preferences. The proposed approach works with a single agent to control 14 household appliances and uses a reduced number of state-action pairs and fuzzy logic for rewards functions to evaluate an action taken for a certain state. The simulation results show that the proposed appliances scheduling approach can smooth the power consumption profile and minimise the electricity cost while considering user’s preferences, user’s feedbacks on each action taken and his/her preference settings. A user-interface is developed in MATLAB/Simulink for the Home Energy Management System (HEMS) to demonstrate the proposed DR scheme. The simulation tool includes features such as smart appliances, electricity pricing signals, smart meters, solar photovoltaic generation, battery energy storage, electric vehicle and grid supply.Peer reviewe

Regional And Residential Short Term Electric Demand Forecast Using Deep Learning

For optimal power system operations, electric generation must follow load demand. The generation, transmission, and distribution utilities require load forecasting for planning and operating grid infrastructure efficiently, securely, and economically. This thesis work focuses on short-term load forecast (STLF), that concentrates on the time-interval from few hours to few days. An inaccurate short-term load forecast can result in higher cost of generating and delivering power. Hence, accurate short-term load forecasting is essential. Traditionally, short-term load forecasting of electrical demand is typically performed using linear regression, autoregressive integrated moving average models (ARIMA), and artificial neural networks (ANN). These conventional methods are limited in application for big datasets, and often their accuracy is a matter of concern. Recently, deep neural networks (DNNs) have emerged as a powerful tool for machine-learning problems, and known for real-time data processing, parallel computations, and ability to work with a large dataset with higher accuracy. DNNs have been shown to greatly outperform traditional methods in many disciplines, and they have revolutionized data analytics. Aspired from such a success of DNNs in machine learning problems, this thesis investigated the DNNs potential in electrical load forecasting application. Different DNN Types such as multilayer perception model (MLP) and recurrent neural networks (RNN) such as long short-term memory (LSTM), Gated recurrent Unit (GRU) and simple RNNs for different datasets were evaluated for accuracies. This thesis utilized the following data sets: 1) Iberian electric market dataset; 2) NREL residential home dataset; 3) AMPds smart-meter dataset; 4) UMass Smart Home datasets with varying time intervals or data duration for the validating the applicability of DNNs for short-term load forecasting. The Mean absolute percentage error (MAPE) evaluation indicates DNNs outperform conventional method for multiple datasets. In addition, a DNN based smart scheduling of appliances was also studied. This work evaluates MAPE accuracies of clustering-based forecast over non-clustered forecasts

Hierarchical and Distributed Architecture for Large-Scale Residential Demand Response Management

The implementation of smart grid brings several challenges to the power system. The ‘prosumer’ concept, proposed by the smart grid, allows small-scale ‘nano-grids’ to buy or sell electric power at their own discretion. One major problem in integrating prosumers is that they tend to follow the same pattern of generation and consumption, which is un-optimal for grid operations. One tool to optimize grid operations is demand response (DR). DR attempts to optimize by altering the power consumption patterns. DR is an integrated tool of the smart grid. FERC Order No. 2222 caters for distributed energy resources, including demand response resources, in participating in energy markets. However, DR contribution of an average residential energy consumer is insignificant. Most residential energy consumers pay a flat price for their energy usage and the established market for residential DR is quite small. In this dissertation, a survey is carried out on the current state-of-the-art in DR research and generalizations of the mathematical models are made. Additionally, a service provider model is developed along with an incentive program and user interfaces (UI). These UIs and incentive program are designed to be attractive and easily comprehended by a large customer base. Furthermore, customer behavior models are developed that characterize the potential customer base, allowing a demand response aggregator to understand and quantify the quality of the customer. Optimization methods for DR management with various characteristics are also explored in this dissertation. Moreover, A scalable demand response management framework that can incorporate millions of participants in the program is introduced. The framework is based on a hierarchical architecture. To improve DR management, hierarchical load forecasting method is studied. Specifically, optimal combination method for hierarchical forecast reconciliation is applied to the DR program. It is shown that the optimal combination for reconciliation of hierarchical predictions could reduce the stress levels of the consumer close to the ideal values for all scenarios

Robust energy disaggregation using appliance-specific temporal contextual information

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%.Peer reviewedFinal Published versio