EIES-Lab

New lab website (test version)

Tue, 20 Oct 2026 00:00:00 +0000

The EIES-Lab website is being rebuilt as a bilingual static site. This is a placeholder news post.

Spatiotemporal deep learning for rooftop PV potential

Thu, 15 Oct 2026 16:00:00 +0000

[Placeholder] Paper accepted

Sat, 10 Oct 2026 00:00:00 +0000

A new paper on energy-aware embodied intelligence has been accepted. Replace with a real publication.

[Placeholder] Welcome new members

Tue, 01 Sep 2026 00:00:00 +0000

We welcome new PhD and MPhil students joining EIES-Lab this autumn. Replace with real names.

City-scale facade solar potential through AIoT, GIS and meteorology synergy

Thu, 20 Aug 2026 10:30:00 +0000

[Placeholder] Invited talk

Wed, 15 Jul 2026 00:00:00 +0000

Dr. Zhiling Guo gave an invited talk on embodied intelligence in urban energy systems. Replace with the real event.

Demo paper 2: replace with a real publication

Wed, 01 Apr 2026 00:00:00 +0000

Shadow-aware rooftop PV model

Mon, 01 Sep 2025 00:00:00 +0000

Model structure

The model takes a height map of the surrounding buildings and the sun position, and predicts the shading on each roof for every hour of the year. A temporal branch then turns hourly shading and weather into PV output.

Why it is fast

Ray-traced shading has to be recomputed for every hour and every building. The learned model replaces that step, which is what makes city-wide runs about 158 times faster than simulation.

Inputs and outputs

Inputs: building heights, roof geometry, sun position, hourly weather.
Outputs: hourly shading masks and rooftop PV potential per building.

Spatiotemporal feature encoded deep learning method for rooftop PV potential assessment

Mon, 01 Sep 2025 00:00:00 +0000

Global estimation of building-integrated facade and rooftop photovoltaic potential by integrating 3D building footprint and spatio-temporal datasets

Sun, 01 Jun 2025 00:00:00 +0000

Global facade & rooftop PV potential dataset

Sun, 01 Jun 2025 00:00:00 +0000

What it contains

For every building in each city, the dataset gives annual and hourly solar irradiation on the roof and on each facade, the usable area after windows and obstructions are removed, and the resulting PV generation potential.

How it is built

Building footprints and heights come from open 3D city models.
Facade elements (windows, walls) are parsed from street-view images.
Hourly irradiation uses local meteorology and the shading cast by neighbouring buildings.

Research uses

The dataset supports city-level planning of building-integrated PV, comparisons between cities, and training data for faster learned models.

Advancing building facade solar potential assessment through AIoT, GIS, and meteorology synergy

Sat, 01 Mar 2025 00:00:00 +0000

Demo paper 1: replace with a real publication

Sat, 01 Mar 2025 00:00:00 +0000

Facade parsing toolkit

Sat, 01 Mar 2025 00:00:00 +0000

DENG Ruizhe