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Time series forecasting applied to an energy management system ‐ A comparison between Deep Learning Models and other Machine Learning Models
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Resumo(s)
A 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.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Artificial Intelligence Big Data Data Science Deep Learning Energy Management System Heat Ventilation Air Conditioning Internet of Things Long Short-Term Memory Machine Learning SDG 12 - Responsible consumption and production