Optimization of the Use of Residential Lighting with Neural Network

Hernández, César; Romero, Ricardo; Giral, Diego

doi:10.1109/cise.2010.5677018

Cited by 11 publications

(8 citation statements)

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“…When multiple devices need to be simultaneously managed, two strategies can be followed. One considers training an individual ANN for each appliance [112], while the other approach considers training a single ANN to control multiple devices [113].…”

Section: ) Appliance Schedulingmentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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Energy is a vital resource for human activities and lifestyle, powering important everyday infrastructures and services. Currently, pollutant and non-renewable sources, such as fossil fuels, remain the main source of worldwide consumed energy. The environmental impact of their exploitation has boosted research and investments in alternative, clean and renewable sources, including photovoltaic and windbased systems. As a whole, buildings are one of the major energy consumption sectors. Hence, improving energy efficiency in buildings will result in economical and environmental gains. In the case of households, home energy management systems are mainly used for monitoring real-time consumption and to schedule appliance operations so that the energy bill could be minimised, or according to another specific criterion. This work aims to survey the most recent literature on home energy management systems, providing an aggregated and unified perspective in the context of residential buildings. In addition, an updated literature list regarding commonly managed household appliances and scheduling objectives are included. Physical and operational constraints, and how they are addressed by home energy management systems along with security issues are also discussed. INDEX TERMS Energy efficiency, home energy management systems, household appliance models, load management, optimal scheduling, smart homes, security.

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Section: ) Appliance Schedulingmentioning

confidence: 99%

A Survey on Home Energy Management

et al. 2020

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“…Reference [129] presents an approach that uses a Neural Network model to determine appliance scheduling. [130] describes a global neural network controller, which takes into account all inputs to switch off the required device. In [131], ANN is used with a genetic algorithm for weekly appliance scheduling.…”

Section: Reinforcement Learningmentioning

confidence: 99%

Home Energy Management System Concepts, Configurations, and Technologies for the Smart Grid

2020

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Home energy management systems (HEMSs) help manage electricity demand to optimize energy consumption and distributed renewable energy generation without compromising consumers' comfort. HEMSs operate according to multiple criteria, including energy cost, weather conditions, load profiles, and consumer comfort. They play an increasingly ubiquitous role in energy efficiency through the reduction of electricity consumption within residential and commercial smart grids. This paper presents a comprehensive review of the HEMS literature with reference to main concepts, configurations, and enabling technologies. In doing so, it also provides a summary of HEMS computing trends and popular communication technologies for demand response applications. The ensuing survey offers the reader with an overall overview of current and future trends in HEMS solutions and technologies. INDEX TERMS Home energy management system, demand response, smart technologies, integrated wireless technology, intelligent scheduling controller. I.

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“…The first lighting controls were created such as on/off switch control or dimming by using sensors' outputs. Also, user-centric models based on occupants' location and their activities were used to define optimal lighting intensity level as a balance between user satisfaction and energy cost [155][156][157].…”

Section: Lighting Operationmentioning

confidence: 99%

Modeling occupant behavior in buildings

Carlucci

Simone

Firth

et al. 2020

Building and Environment

155

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In the last four decades several methods have been used to model occupants' presence and actions (OPA) in buildings according to different purposes, available computational power, and technical solutions. This study reviews approaches, methods and key findings related to OPA modeling in buildings. An extensive database of related research documents is systematically constructed, and, using bibliometric analysis techniques, the scientific production and landscape are described. The initial literature screening identified more than 750 studies, out of which 278 publications were selected. They provide an overarching view of the development of OPA modeling methods. The research field has evolved from longitudinal collaborative efforts since the late 1970s and, so far, covers diverse building typologies mostly concentrated in a few climate zones. The modeling approaches in the selected literature are grouped into three categories (rule-based models, stochastic OPA modeling, and data-driven methods) for modeling occupancy-related target functions and a set of occupants' actions (window, solar shading, electric lighting, thermostat adjustment, clothing adjustment and appliance use). The explanatory modeling is conventionally based on the model-based paradigm where occupant behavior is assumed to be stochastic, while the Revised Manuscript with No Changes MarkedClick here to view linked References data-driven paradigm has found wide applications for the predictive modeling of OPA, applicable to control systems. The lack of established standard evaluation protocols was identified as a scientifically important yet rarely addressed research question. In addition, machine learning and deep learning are emerging in recent years as promising methods to address OPA modeling in real-world applications.

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Optimization of the Use of Residential Lighting with Neural Network

Cited by 11 publications

References 1 publication

A Survey on Home Energy Management

A Survey on Home Energy Management

Home Energy Management System Concepts, Configurations, and Technologies for the Smart Grid

Modeling occupant behavior in buildings

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