A Method for Short-Term Prediction of the Metro Station’s Individual Energy Consumption Item Based on G-ACO-BP Model

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 3474077
• Main Authors: Sha, Guorong, Qian, Qing
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Air conditioning; Analysis; Ant colony optimization; Back propagation networks; Eigenvalues; Energy consumption; Energy use; Fault diagnosis; Genetic algorithms; Genetic analysis; Lighting; Methods; Neural networks; Neural Networks, Computer; Optimization; Power consumption; Predictions; Principal Component Analysis; Principal components analysis; Social networks; Subway stations; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2021/3474077

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated air conditioning in the metro station. First, the paper extracts the five main factors influencing metro station energy consumption through the kernel principal component analysis (KPCA). Second, improved genetic-ant colony optimization (G-ACO) was fused into the BP neural network to train and optimize the connection weights and thresholds between each BP neural network layer. The paper then builds a G-ACO-BP neural model to make short-term predictions about different energy consumption in the metro station to predict the energy consumed by power, lighting, and ventilated air conditioning. The experimental results showed that the G-ACO-BP neural model could give a more accurate and effective prediction for the main energy consumption in a metro station.