Synthetic Building Dataset for Recursive Feed Forward Correlation Network

Figshare 2026 Dataset Open access

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Authors: Ashok J
DOI: 10.6084/m9.figshare.31422440
Cited by: 0

Abstract

This dataset contains 1000 synthetic records representing building environment variables. It was generated using Python (NumPy, Pandas) with random distributions to simulate typical indoor conditions. This dataset is intended for demonstration of predictive modeling, Recursive Feedforward Correlation Networks (RFCN), and concept drift resilience in building sciences.

Fields of Study & Subjects

Building Energy and Comfort Optimization Neural Networks and Applications Data Stream Mining Techniques

Publication details

Journal / source
Figshare
Publication date
26 February 2026
Language
EN
Document type
Dataset