A data-driven methodology to predict thermal behavior of residential buildings using piecewise linear models. (November 2020)

• Record Type: Journal Article
• Authors: Benzaama, M.H.; Rajaoarisoa, L.H.; Ajib, B.; Lecoeuche, S.
• Is Part Of: Journal of building engineering. Volume 32(2021)
• Publication Date: 2020-11
• Subjects: Thermal behavior -- Smart-metering -- Data-driven models -- PWARX -- Experiments on real buildings; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2020.101523
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Nowadays, data-driven approaches are a good way to estimate very efficient black box models for different engineering systems. This class of model is well recognized by its outstanding performances to describe the overall behavior of one system based on its input-output relationships without any physical knowledge. In the context of building modeling, this approach is particularly well suited to predict future temperatures or energy consumption in a building. This paper presents an innovative method that uses input-output data to establish reliable and suitable thermal behavior models for residential buildings, especially for existing buildings where only measurements are available and no numerical models are at the disposal of the facility managers. The main paper contributions consist in the design of a new methodology based on the adaptation of a switched model estimation technique and in its validation to model accurately building thermal behaviors. The paper describes different stages needed to reproduce faithfully complex behaviors: data collection, PieceWise affine Auto-Regressive eXogenous (PWARX) identification technique, sensitivity analysis … It also explains how the procedure and the data-driven estimation algorithm are efficient in extracting sub-model parameters and sequence that give an outstanding ability to reproduce thermal dynamics of buildings, requiring the only collection of available data. The effectiveness of our methodology is discussed through experiments on different buildings located in the North of France. Indeed, through a comparative study between the piecewise ARX model and other existing models such as nonlinear ARX, indexed ARX and ARX models, the PWARX model gives good results in terms of indoor temperature estimation with 78.48 % accuracy. Highlights: Development of a new model based on the data driven method. .The paper conducts a comprehensive review of data-driven modeling. .Data-driven models as ARX, indexed ARX and NARX are critically compared. .Methods for model identification and sensitivity analysis are summarized. The developed model is validated using on-site data.