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Time series forecasting applied to an energy management system ‐ A comparison between Deep Learning Models and other Machine Learning Models

2020-11-13Dissertação de mestrado Acesso aberto
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dc.contributor.advisorCastelli, Mauro
dc.contributor.authorArienti, João Henrique Leal
dc.date.accessioned2020-12-04T15:04:53Z
dc.date.available2020-12-04T15:04:53Z
dc.date.issued2020-11-13
dc.descriptionProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analyticspt_PT
dc.description.abstractA large amount of energy used by the world comes from buildings’ energy consumption. HVAC (Heat, Ventilation, and Air Conditioning) systems are the biggest offenders when it comes to buildings’ energy consumption. It is important to provide environmental comfort in buildings but indoor wellbeing is directly related to an increase in energy consumption. This dilemma creates a huge opportunity for a solution that balances occupant comfort and energy consumption. Within this context, the Ambiosensing project was launched to develop a complete energy management system that differentiates itself from other existing commercial solutions by being an inexpensive and intelligent system. The Ambiosensing project focused on the topic of Time Series Forecasting to achieve the goal of creating predictive models to help the energy management system to anticipate indoor environmental scenarios. A good approach for Time Series Forecasting problems is to apply Machine Learning, more specifically Deep Learning. This work project intends to investigate and develop Deep Learning and other Machine Learning models that can deal with multivariate Time Series Forecasting, to assess how well can a Deep Learning approach perform on a Time Series Forecasting problem, especially, LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNN) and to establish a comparison between Deep Learning and other Machine Learning models like Linear Regression, Decision Trees, Random Forest, Gradient Boosting Machines and others within this context.pt_PT
dc.identifier.tid202545768pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/108172
dc.language.isoengpt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArtificial Intelligencept_PT
dc.subjectBig Datapt_PT
dc.subjectData Sciencept_PT
dc.subjectDeep Learningpt_PT
dc.subjectEnergy Management Systempt_PT
dc.subjectHeatpt_PT
dc.subjectVentilationpt_PT
dc.subjectAir Conditioningpt_PT
dc.subjectInternet of Thingspt_PT
dc.subjectLong Short-Term Memorypt_PT
dc.subjectMachine Learningpt_PT
dc.subjectSDG 12 - Responsible consumption and production
dc.titleTime series forecasting applied to an energy management system ‐ A comparison between Deep Learning Models and other Machine Learning Modelspt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameMestrado em Métodos Analíticos Avançadospt_PT

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NIMS - Dissertações de Mestrado em Ciência de Dados e Métodos Analíticos Avançados (Data Science and Advanced Analytics)