Urban micro-climate prediction through long short-term memory network with long-term monitoring for on-site building energy estimation. (November 2021)

• Record Type: Journal Article
• Authors: Zhang, Muxing; Zhang, Xiaosong; Guo, Siyi; Xu, Xiaodong; Chen, Jiayu; Wang, Wei
• Is Part Of: Sustainable cities and society. Volume 74(2021)
• Publication Date: 2021-11
• Subjects: Urban micro-climate -- Long short-term memory -- Meteorological prediction -- Building energy estimation; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103227
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: Mico-climates were predicted using long short-term memory (LSTM) neural network. EnergyPlus weather (EPW), weather from climate station, and on-site measured and predicted climate were compared. Build energy estimation based on different meteorological inputs was conducted and analyzed. Heating and cooling loads error metrics were evaluated to reveal the propagation of LSTM prediction error. Abstract: Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.