Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection

Jian Sun, Gang Liu, Boyang Sun and Gang Xiao

Applied Energy, 2021, vol. 303, issue C, No S0306261921010540

Abstract: Building energy consumption prediction plays an irreplaceable role in energy resource management and planning. Continuous improvement in the performance of predictive models is the key to ensure energy management and deployment operations. The imbalance between the speed and the accuracy for hyperparameter optimization is an important factor that limits the performance of the model. A Light-Stacking Strengthened Fusion Framework (LSStFu) is proposed to solve this problem. The optimization and fusion of the multi-type hyperparameter model obtained by random search can greatly improve the accuracy of the model prediction. This process can also assure a reduction in time consumption when compared with grid search. Moreover, a feature selection algorithm can only describe a single aspect of a bunch of multiple types of data sets, which limits the generalization performance ability of the algorithm. In order to address the above limitation, a Variable Weight Feature Selection (VWFS) method is proposed to fuse the contribution of three feature selection algorithms based on particle swarm optimization. To evaluate the robustness of the proposed LSStFu algorithm, it is compared with other algorithms through eight evaluation indicators. This evaluation process verifies the reliability and stability of the Light-Stacking framework to provide an efficient, accurate, and stable hyperparameter optimization framework for energy predictive models. From the ablation analysis, it can be observed that with the optimal subset of features obtained through the VWFS, the accuracy of the prediction models is improved. In addition, the model construction process has also sped up at the same time.

Keywords: Short-term building energy forecasting; Integrated learning algorithm; Light-Stacking Strengthened Fusion Framework; Variable Weight Feature Selection (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261921010540
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:303:y:2021:i:c:s0306261921010540

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2021.117694

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().