This paper presents a study of wind speeds for heights of 100 and 120 m above Hera mountain which is located in Dili. The results of wind speed simulation from January to December 2014 are used as input data to estimate wind energy. The weather research and forecasting (WRF) model is used to simulate one year's wind speed for nesting domain 1×1 km at 100 and 120 m heights above Hera mountain. It is done using six-hourly interval 1 • × 1 • NCEP FNL analysis data for initial simulation. The system advisor model version SAM 2017.9.5 is used to estimate wind energy performance with the power purchase agreement (single owner) model. The results show the lowest average wind speed is 3.83 m/s obtained in November with an average monthly energy of 3.8 GWh for 100 m height and 5.57 GWh for 120 m height. The highest average monthly wind energy is 33.9 GWh obtained in January for 100 m height and 50 GWh for 120 m height when average wind speeds from both heights reached ±9 m/s. Finally, it is concluded that the WRF model performed with SAM is a good combination to simulate local wind speed and wind energy for local consumption.

Three months comparison of hourly solar radiation forecasting from 1st January to 31st March 2017 between Weather Research and Forecasting (WRF) mesoscale model and Long short-term memory (LSTM) algorithm is presented in this study. One-way grid nesting technique of the WRF model is applied for the simulation with a six-hourly input dataset downloaded from the National Oceanic and Atmospheric Administration -National Operational Model Archive and Distribution System (NOMADS) website. Three years'data of solar radiation from 1st January 2014 to 31st December 2016 are used as input data for Long Short Term Memory (LSTM) algorithm to simulate solar radiation. The results show the root mean square error of the LSTM algorithm is 310 W m −2 higher compared to 210 W m −2 from the WRF model. The MBE and the nMBE of the WRF model are obtained positive value 96 W m −2 and 9% compared to −101 W m −2 and −9% of LSTM for 2160 h prediction. Meanwhile, the performance error percentage of WRF is 19% lower compared to 28% of LSTM for the nRMSE error metric. Although this study found that the WRF model performed better and lower error compared to the LSTM algorithm, however, it also recommends the LSTM algorithm configuration can be used for long-term prediction.

Climatic chambers are of great importance in research and development to conduct tests of components in closed environmentally controlled conditions. The growing demand from the industry to fulfill stricter international standards creates the necessity to ensure that the thermofluidic behavior of climatic chambers guarantees high-quality consistency in their interior domain. At present, scientific research on climatic chambers available in the literature is scarce and is mostly based on lumped modeling, hence not addressing the heterogeneities that arise in the interior of the chamber. In this work, an in-depth 3D model of the velocity and temperature fields that develops in the interior of climatic chambers was developed in computer fluid dynamics (CFD) and validated with the experimental data from a new prototype. The key objective of this research was to establish a validated framework for model-based design optimization of climatic chambers. The proposed model showed good agreement with the experimental data with a difference of 0.6 m/s and 9.65 °C in the velocity and temperature fields, respectively, thus validating its applicability to perform model-based design optimization of climatic chambers.

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