Design of a Home Energy Management System by Online Neural Networks

Ciabattoni, Lucio; Ippoliti, Gianluca; Benini, Alessandro; Longhi, Sauro; Pirro, Matteo

doi:10.3182/20130703-3-fr-4038.00111

Cited by 25 publications

(24 citation statements)

References 34 publications

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“…Ermes et al [102] proposed a hybrid classifier approach using a tree structure comprising a priori knowledge and ANN to recognize the activities such as rowing, biking, playing football, walking, running, sitting, or hiking. Ciabattoni et al [103] proposed a home energy management system design using the neural network algorithm to predict the power production of the photovoltaic plant and the home consumptions during the given time. Deep learning is now ubiquitously used in major businesses and companies.…”

Section: D) Support Vector Machine (Svm)mentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart buildings includes making the residents' experience as easy and comfortable as possible. The massive streaming data generated and captured by smart building appliances and devices contains valuable information that needs to be mined to facilitate timely actions and better decision making. Machine learning and big data analytics will undoubtedly play a critical role to enable the delivery of such smart services. In this paper, we survey the area of smart building with a special focus on the role of techniques from machine learning and big data analytics. This survey also reviews the current trends and challenges faced in the development of smart building services.

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Section: D) Support Vector Machine (Svm)mentioning

confidence: 99%

Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey

et al. 2019

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“…In particular since in this model we represent the typical user behavior, for what regards the starting of one of these two tasks we consider the best time to start the appliance according to the algorithm described in [32]. 6 − seconds resolution data of most of the household appliances (e.g.…”

Section: Model Implementationmentioning

confidence: 99%

Home energy management benefits evaluation through fuzzy logic consumptions simulator

Ciabattoni

Grisostomi

Ippoliti

et al. 2014

2014 International Joint Conference on Neural Networks (IJCNN)

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In recent years the European Union and, moreover, Italy has seen a rapid growth in the photovoltaic (PV) sector, following the introduction of the feed in tariff (FIT) scheme known as Conto Energia. In July 2013 the Italian government definitively cut FITs, leaving only tax benefits and a revised net metering scheme (known as "Scambio sul Posto") for new PV installations. In this scenario, the design of a new PV plant ensuring savings on electricity bills is strongly related to household electricity consumption patterns. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Using as inputs patterns of active occupancy and typical domestic habits, the fuzzy model give as output the likelihood to start each appliance within the next minute. The focus of this work is the use of this novel fuzzy model to correctly size a residential photovoltaic plant and evaluate the economic benefits of energy management actions in a case study. A cost benefits analysis is presented to quantify its effectiveness in the new net metering Italian scenario.

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“…Accordingly in [20] authors use a neural network based approach to obtain individual loads profiles forecasts from the frequency contents of the global load curve of the household. In [21] authors proposes to forecast up to 24 hours ahead electrical consumptions and PV production in order to develop an intelligent load scheduling algorithm. Most of the existing models and analysis focus on data from specific geographic regions and try to explain the results in a local perspective (e.g.…”

Section: Introductionmentioning

confidence: 99%