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.
Highlights
Urbanization brings the rapid growth of the building floor area and inevitably causes an explosive increase in energy consumption [1,2]
As building load forecasting is significant to the district energy system in the early design stage, researchers around the world have put forward many forecasting methods, which can be roughly categorized into two types: the building-physics-based method and data-driven method [7]
Compared with the MAPE values evaluated in the referred paper [25], which are 20.2–29.6% for forecasting district heating load profile and 6.6–8.8% for forecasting electricity load profile, the MAPE results in this paper show that the simulated load data from physics-based meta-models can be treated as an effective alternative when the recorded energy data are unavailable
Summary
Urbanization brings the rapid growth of the building floor area and inevitably causes an explosive increase in energy consumption [1,2]. As building load forecasting is significant to the district energy system in the early design stage, researchers around the world have put forward many forecasting methods, which can be roughly categorized into two types: the building-physics-based method and data-driven method [7]. The input data for the building-physics-based method includes building information, thermal characteristics of envelopes, outdoor and indoor temperatures, ventilation rates, energy consumption of appliances, the number of occupants, etc. In view of the above shortcomings of conventional methods, this paper puts forward a meta-modeling workflow to forecast all-year hourly building loads based on the deep learning method. Referring to an existing study of indispensable input variables for predicting building energy consumption [26], this paper selects seven building feature variables to establish the meta-model database. 0.1, 0.4, 0.6, 0.8 22, 24, 26, 28 16, 18, 20, 21 2, 5, 7, 10 (for office and hotel) 10, 13, 16, 20 (for commercial)
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