Data driven prediction models of energy use of appliances in a low-energy house

By Luis M. Ibarra Candanedo, Veronique Feldheim, Dominique Deramaix. 2017

557 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering

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Citations in the corpus

Adaptive Federated Learning in Resource Constrained Edge Computing Systems (2018, methodology): “Linear regression is performed with SGD on the energy dataset [44], which contains 19, 735 records of measurements from multiple sensors and the energy consumptions of appliances and lights.”
Low-Cost Recurrent Neural Network Expected Performance Evaluation (2018, methodology): “Then, we studied three use cases: (i) a sine wave, (ii) the occupancy rate of 29 car parks located in Birmingham, UK, and (iii) the appliances energy consumption of a house in Stambruges, Belgium, and the nearby weather data [10].”
A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, methodology): “Therefore, it is usually utilized in three cases: (1) Doing single-step prediction [56], [116]; (2) Doing multi-step prediction with longer horizon (usually higher than 1 day) and larger scale (generally greater than district).”
Parallel spatio-temporal attention-based TCN for multivariate time series prediction (2021, background): “No area of activity escapes our desire to prepare, proﬁt or prevent through forecasting, be it ﬁnance forecasting [3], weather forecasting [4], [5], human activity detection [8], energy consumption prediction [10], industrial fault diagnosis [41] and etc..”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, methodology): “It contains the dataset of the UK Low Carbon London trial [210] and the UCI datasets [36, 98].”