In order to assess the effectiveness of the proposed Spatial-Temporal Graph Neural Network model, a model comparison was conducted, considering alternative data-driven and regression-based approaches commonly employed in building energy ...
With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application.
Produced in cooperation with the International Building Performance Simulation Association (IBPSA), and featuring contributions from fourteen internationally recognised experts in this field, this book provides a unique and comprehensive ...
Energy demand forecasting has become a relevant subject in the energy management field.
These forecasts are used to minimize unit power production costs for the energy managers for better planning of power units and load management. In this work, three different state-of-art machine learning methods i.e.
... M .; Zhao , X. A review of data - driven approaches for prediction and classification of building energy consumption ... hourly cooling load in the building : A comparison of support vector machine and different artificial neural ...
... Building Energy Consumption Prediction Studies[J]. Renew. Sust. Energ. Rev. 81. doi:10.1016/j.rser.2017.04.095 de ... Machine Learning Algorithms: MGGP and MLP Neural Networks[J]. Energies 13 (11). Dong, B., Li, Z., Rahman ...
... energy gain factor for single-family buildings. Build Environ 37(11):1019– tion with hybrid genetic algorithm-hierarchical adap- tive network-based fuzzy inference system. Energy Build 42(11):2070–2076 1026 Panapakidis IP, Papadopoulos ...
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international ...
Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data.