Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models

Ferrández-Pastor, Francisco-Javier; Mora, Higinio; Sánchez-Romero, José-Luis; Nieto-Hidalgo, Mario; García‐Chamizo, Juan Manuel

doi:10.1007/s12652-016-0431-y

Cited by 27 publications

(12 citation statements)

References 37 publications

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“…In energy demand modeling, stochastic processes such as Markov chains are used for forecasting and simulation of load profiles [20,[44][45][46]. Fuzzy logic is employed in the form of fuzzy time series [47], fuzzy regression models [5], fuzzy clustering [48], and adaptive neuro-fuzzy interference systems (ANFIS) [49].…”

Section: Stochastic Fuzzy and Grey Systems Theory Techniquesmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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In this article, a systematic literature review of 419 articles on energy demand modeling, published between 2015 and 2020, is presented. This provides researchers with an exhaustive overview of the examined literature and classification of techniques for energy demand modeling. Unlike in existing literature reviews, in this comprehensive study all of the following aspects of energy demand models are analyzed: techniques, prediction accuracy, inputs, energy carrier, sector, temporal horizon, and spatial granularity. Readers benefit from easy access to a broad literature base and find decision support when choosing suitable data-model combinations for their projects. Results have been compiled in comprehensive figures and tables, providing a structured summary of the literature, and containing direct references to the analyzed articles. Drawbacks of techniques are discussed as well as countermeasures. The results show that among the articles, machine learning (ML) techniques are used the most, are mainly applied to short-term electricity forecasting on a regional level and rely on historic load as their main data source. Engineering-based models are less dependent on historic load data and cover appliance consumption on long temporal horizons. Metaheuristic and uncertainty techniques are often used in hybrid models. Statistical techniques are frequently used for energy demand modeling as well and often serve as benchmarks for other techniques. Among the articles, the accuracy measured by mean average percentage error (MAPE) proved to be on similar levels for all techniques. This review eases the reader into the subject matter by presenting the emphases that have been made in the current literature, suggesting future research directions, and providing the basis for quantitative testing of hypotheses regarding applicability and dominance of specific methods for sub-categories of demand modeling.

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Section: Stochastic Fuzzy and Grey Systems Theory Techniquesmentioning

confidence: 99%

Modeling Energy Demand—A Systematic Literature Review

et al. 2021

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“…For example, energy management [ 8 ] or analysis of accessibility in smart cities [ 9 ]. In the area of healthcare, devices are being designed for many purposes such as patient monitoring to help them manage particularly chronic conditions [ 10 ], recovering from injuries [ 11 ] or design of Ambient Assisted Living (AAL) environments [ 12 ]. Related with this idea, mobile health things (m-health) is a new concept of using smart mobile devices to create efficient healthcare services and solutions [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Mora

Gil

Terol

et al. 2017

Sensors

143

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The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

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“…A very important process at the core of smart environments is the sensor-based activity recognition [1][2][3]. This kind of activity recognition is based on recognizing the actions of one or more persons within an intelligent environment by using a flow of observed events that depend only on the current activity.…”

Section: Introductionmentioning

confidence: 99%

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Salguero

Espinilla

2018

Computers & Electrical Engineering

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In recent years, many techniques have been proposed for automatic recognition of Activities of Daily Living from smart home sensor data. However, classifiers usually use features created ad hoc. In this work, the use of ontologies is proposed for the fully automatic generation of these features. The process consists of converting the original dataset into an ontology and then combine all the concepts and relations in that ontology to obtain relevant class expressions. The high formalization of ontologies allows us to reduce the search space by discarding many meaningless expressions, such as contradictory or unsatisfiable expressions. The relevant class expressions are then used as features by the classifiers to build the classification model. To validate our proposal, we have used as reference the results obtained by four different classification algorithms that use the most commonly used features.

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Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models

Cited by 27 publications

References 37 publications

Modeling Energy Demand—A Systematic Literature Review

Modeling Energy Demand—A Systematic Literature Review

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

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