Machine learning models have proven to be reliable methods in the forecasting of energy use in commercial and office buildings. However, little research has been done on energy forecasting in dwellings, mainly due to the difficulty of obtaining household level data while keeping the privacy of inhabitants in mind. Gaining insight into the energy consumption in the near future can be helpful in balancing the grid and insights in how to reduce the energy consumption can be received. In collaboration with OPSCHALER, a measurement campaign on the influence of housing characteristics on energy costs and comfort, several machine learning models were compared on forecasting performance and the computational time needed. Nine months of data containing the mean gas consumption of 52 dwellings on a one hour resolution was used for this research. The first 6 months were used for training, whereas the last 3 months were used to evaluate the models. The results showed that the Deep Neural Network (DNN) performed best with a 50.1 % Mean Absolute Percentage Error (MAPE) on a one hour resolution. When comparing daily and weekly resolutions, the Multivariate Linear Regression (MVLR) outperformed other models, with a 20.1 % and 17.0 % MAPE, respectively. The models were programmed in Python.
ReSSInt aims at investigating the use of silent speech interfaces (SSIs) for restoring communication to individuals who have been deprived of the ability to speak. SSIs are devices which capture non-acoustic biosignals generated during the speech production process and use them to predict the intended message. Two are the biosignals that will be investigated in this project: electromyography (EMG) signals representing electrical activity driving the facial muscles and invasive electroencephalography (iEEG) neural signals captured by means of invasive electrodes implanted on the brain. From the whole spectrum of speech disorders which may affect a person's voice, ReSSInt will address two particular conditions: (i) voice loss after total laryngectomy and (ii) neurodegenerative diseases and other traumatic injuries which may leave an individual paralyzed and, eventually, unable to speak. To make this technology truly beneficial for these persons, this project aims at generating intelligible speech of reasonable quality. This will be tackled by recording large databases and the use of state-of-the-art generative deep learning techniques. Finally, different voice rehabilitation scenarios are foreseen within the project, which will lead to innovative research solutions for SSIs and a real impact on society by improving the life of people with speech impediments.
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