Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications. © International Building Performance Simulation Association, 2022

Camera-Based Blood Pressure Estimation via Windkessel Model and Waveform Features

Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problems using remote photoplethysmography (rPPG), which has shown great success in heart rate detection. Nevertheless, these approaches are not robust, and few have been evaluated with a sufficiently large dataset. We propose an rPPG-based BP estimation algorithm that predicts BP by leveraging the Windkessel model and hand-crafted waveform characteristics. A waveform processing procedure is presented for the rPPG signals to obtain a robust waveform template and thus extract BP-related features. Redundant and unstable features are eliminated via Monte Carlo simulation and according to their relationship with latent parameters (LSs) in the Windkessel model. For a comprehensive evaluation, the Chiao Tung BP (CTBP) dataset was constructed. The experiment was conducted over a four-week period of time to evaluate the validity period of the personalization in our system. On all the data, the proposed method outperforms the benchmark algorithms and yields mean absolute errors (MAEs) of 6.48 and 5.06 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. The performance achieves a 'B' grade according to the validation protocol from the British Hypertension Society (BHS) for both SBP and DBP. © 1963-2012 IEEE.