The increasing availability of airborne Light Detection And Ranging (LiDAR) data provides new opportunities for environmental simulations. This paper proposes a new method for wind resource assessment by using wind simulation over 3D geometry extracted from classified LiDAR data. The simulation of wind flow is performed by using Smoothed Particle Hydrodynamics (SPH) in two phases for each time-step, firstly over low-resolution Digital Elevation Model (DEM) data, and secondly over high-resolution LiDAR data. Inlet wind particles depend on the logarithmic wind profile, where the morphometric aerodynamic roughness length is considered. The estimated wind power is integrated over a given timespan, resulting in wind energy potential. The simulated velocities were validated with annual measurements, where an agreement of 80.72% and 90.81% was achieved for -0.5 km2 sized urban and rural areas, respectively. The cumulative wind energy potential at 20 m above surface height is at 7.62 GWh and 56.613 GWh for the given areas, respectively.
COBISS.SI-ID: 20593686
This paper considers the use of interpolative coding for lossless chain code compression. The most popular chain codes are used, including Freeman chain code in eight (F8) and four directions (F4), Vertex Chain Code (VCC), and three-orthogonal chain code (3OT). The whole compression pipeline consists of the Burrows–Wheeler transform, Move-To-Front transform and the interpolative coding, which was improved by FELICS and new ?-coding. The approach was compared with the state-of-the-art chain code compression algorithms. For VCC, 3OT and F4, the obtained results are slightly better than the existing approaches. However, an important improvement was achieved with F8 chain code, where the presented approach is considerably better.
COBISS.SI-ID: 20898838
With the growing urbanization and environmental concerns over buildings' energy consumption and carbon footprint, the demand for energy-efficient building design is greater than ever. This paper addresses these concerns by presenting a novel method for estimating and optimising the thermal load (i.e. total energy load for heating and cooling) of a building within a real environment, provided by high-resolution LiDAR data, while considering long-term climatological parameters, estimated direct and anisotropic diffuse irradiance, shadowing from surroundings, and terrain topography. In the optimisation part of the method, the building's design is optimised regarding the estimated thermal load. The estimation was validated with the well-established EnergyPlus software. In experiments, a rectangular building's design was optimised on a flat and urban dataset. The effect of a building's design parameters on thermal load was inspected as well. On average, the proposed method improved a building's net heat gain by over 103 kWh/m2 and reduced its thermal load by 234.18 kWh/m2 when compared with the initial building design.
COBISS.SI-ID: 20948246