A Deep-Learning-Based Meta-Modeling Workflow for Thermal Load Forecasting in Buildings: Method and a Case Study

Abstract

This paper proposes a meta-modeling workflow to forecast the cooling and heating loads of buildings at individual and district levels in the early design stage. Seven input variables, with large impacts on building loads, are selected for designing meta-models to establish the MySQL database. The load profiles of office, commercial, and hotel models are simulated with EnergyPlus in batches. A sequence-to-sequence (Seq2Seq) model based on the deep-learning method of a one-dimensional convolutional neural network (1D-CNN) is introduced to achieve rapid forecasting of all-year hourly building loads. The method performs well with the load effective hour rate (LEHR) of around 90% and MAPE less than 10%. Finally, this meta-modeling workflow is applied to a district as a case study in Shanghai, China. The forecasting results well match the actual loads with R2 of 0.9978 and 0.9975, respectively, for the heating and cooling load. The LEHR value of all-year hourly forecasting loads is 98.4%, as well as an MAPE of 4.4%. This meta-modeling workflow expands the applicability of building-physics-based methods and improves the time resolution of conventional data-driven methods. It shows small forecasting errors and fast computing speed while meeting the required precision and convenience of engineering in the building early design stage.

Bayesian Optimization-Based LSTM for Short-Term Heating Load Forecasting

“…The various methods mentioned above provide scientific guidance for heat load prediction [15]. Among them, machine learning methods are more popular in heat load forecasting due to their high accuracy and flexibility [16].…”

Decarbonization potentials of the embodied energy use and operational process in buildings: A review from the life-cycle perspective

“…Considering that real-world data may be unavailable in some cases (e.g., evaluation for the scheme design), the BPS technology has promoted the feasibility of building OCEs assessment and prediction [ 93 ]. BPS methods can be classified into physical-based and data-driven ones [ 94 ]. The physical-based methods use the building simulation tools (e.g., DOE [ 75 ], EnergyPlus [ 77 ], e-Quest [ 73 ], and DesignBuilder [ 14 ]) to generate building-physics models and complete the numerical analysis of energy performance.…”

Image Retrieval for Local Architectural Heritage Recommendation Based on Deep Hashing

“…With the rapid development of convolutional neural networks (CNNs) [10,11], DL [12][13][14] methods have achieved many encouraging results in heritage image analysis [15,16]. Among them, CBIR [17,18], which searches for images from a large image collection, can serve as a recommendation tool for local architectural heritage.…”