Yangyang Fu

TL;DR: This paper reviews the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.

TL;DR: In this article, the authors summarize the progress made in the theoretical modeling of gradient metamaterials, in their experimental implementation and in the design of functional devices for wave bending and focusing in free space, for supporting surface plasmon polaritons and for the realization of trapped rainbows.

TL;DR: A refractive-type metagrating which can enable anomalous reflection and refraction with almost unity efficiency over a wide incident range is shown and how integer parity plays a role in higher order diffraction is uncovered.

TL;DR: In this article, the authors proposed a software tool to analyze the feasibility of the fifth generation of district heating and cooling systems in both new and existing districts, which is characterized by supply temperatures in the ambient range of 15-25°C, which not only reduces heat loss but also integrates various kinds of low-temperature waste heat sources.

TL;DR: In this paper, a method based on Support Vector Machine (SVM) is proposed to predict the loads at system level for predicting short-term electricity load accurately, which is critical to facilitate demand side management in the building sector.