7,605 research outputs found
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
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Multi-time-horizon Solar Forecasting Using Recurrent Neural Network
The non-stationarity characteristic of the solar power renders traditional
point forecasting methods to be less useful due to large prediction errors.
This results in increased uncertainties in the grid operation, thereby
negatively affecting the reliability and increased cost of operation. This
research paper proposes a unified architecture for multi-time-horizon
predictions for short and long-term solar forecasting using Recurrent Neural
Networks (RNN). The paper describes an end-to-end pipeline to implement the
architecture along with the methods to test and validate the performance of the
prediction model. The results demonstrate that the proposed method based on the
unified architecture is effective for multi-horizon solar forecasting and
achieves a lower root-mean-squared prediction error compared to the previous
best-performing methods which use one model for each time-horizon. The proposed
method enables multi-horizon forecasts with real-time inputs, which have a high
potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE
2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i
Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data
Accurate day-ahead load forecasting is an important task in smart energy communities, as it enables improved energy management and operation of flexibilities. Smart meter data from individual households within the communities can be used to improve such forecasts. In this study, we introduce a novel hybrid bi-directional LSTM-XGBoost model for energy community load forecasting that separately forecasts the general load pattern and peak loads, which are later combined to a holistic forecasting model. The hybrid model outperforms traditional energy community load forecasting based on standard load profiles as well as LSTM-based forecasts. Furthermore, we show that the accuracy of energy community day-ahead forecasts can be significantly improved by using smart meter data as additional input features
Integration of Legacy Appliances into Home Energy Management Systems
The progressive installation of renewable energy sources requires the
coordination of energy consuming devices. At consumer level, this coordination
can be done by a home energy management system (HEMS). Interoperability issues
need to be solved among smart appliances as well as between smart and
non-smart, i.e., legacy devices. We expect current standardization efforts to
soon provide technologies to design smart appliances in order to cope with the
current interoperability issues. Nevertheless, common electrical devices affect
energy consumption significantly and therefore deserve consideration within
energy management applications. This paper discusses the integration of smart
and legacy devices into a generic system architecture and, subsequently,
elaborates the requirements and components which are necessary to realize such
an architecture including an application of load detection for the
identification of running loads and their integration into existing HEM
systems. We assess the feasibility of such an approach with a case study based
on a measurement campaign on real households. We show how the information of
detected appliances can be extracted in order to create device profiles
allowing for their integration and management within a HEMS
Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations
The increased digitalisation and monitoring of the energy system opens up
numerous opportunities to decarbonise the energy system. Applications on low
voltage, local networks, such as community energy markets and smart storage
will facilitate decarbonisation, but they will require advanced control and
management. Reliable forecasting will be a necessary component of many of these
systems to anticipate key features and uncertainties. Despite this urgent need,
there has not yet been an extensive investigation into the current
state-of-the-art of low voltage level forecasts, other than at the smart meter
level. This paper aims to provide a comprehensive overview of the landscape,
current approaches, core applications, challenges and recommendations. Another
aim of this paper is to facilitate the continued improvement and advancement in
this area. To this end, the paper also surveys some of the most relevant and
promising trends. It establishes an open, community-driven list of the known
low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape
Holistic Measures for Evaluating Prediction Models in Smart Grids
The performance of prediction models is often based on "abstract metrics"
that estimate the model's ability to limit residual errors between the observed
and predicted values. However, meaningful evaluation and selection of
prediction models for end-user domains requires holistic and
application-sensitive performance measures. Inspired by energy consumption
prediction models used in the emerging "big data" domain of Smart Power Grids,
we propose a suite of performance measures to rationally compare models along
the dimensions of scale independence, reliability, volatility and cost. We
include both application independent and dependent measures, the latter
parameterized to allow customization by domain experts to fit their scenario.
While our measures are generalizable to other domains, we offer an empirical
analysis using real energy use data for three Smart Grid applications:
planning, customer education and demand response, which are relevant for energy
sustainability. Our results underscore the value of the proposed measures to
offer a deeper insight into models' behavior and their impact on real
applications, which benefit both data mining researchers and practitioners.Comment: 14 Pages, 8 figures, Accepted and to appear in IEEE Transactions on
Knowledge and Data Engineering, 2014. Authors' final version. Copyright
transferred to IEE
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