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This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

• A multi-model and multi-method ensemble approach for wind resource projection is proposed. • Future wind potential in Hong Kong is assessed for the first time. • Robustness of the climate change signal is evaluated. • An increase in wind power density during summer (+23.7 %) and a decrease during autumn (–15.1 %) in 2081–2100 are projected for SSP5-8.5. Harvesting renewable wind energy is among the most cost-efficient means of reducing carbon emissions and achieving carbon neutrality. However, wind resource is susceptible to climate change impacts since the global temperature increase will reshape atmospheric circulation patterns. To facilitate the evaluation of fine-scale wind energy potential under climate variability, this paper proposes and validates a multi-model and multi-method ensemble wind resource projection approach. Then this approach is utilized to investigate the future variation of wind resources in Hong Kong based on the combined use of global climate models from Coupled Model Intercomparison Project Phase 6 and long-term observations from meteorological stations. It is found that there is a significant increase in future wind resources during summer, while a remarkable decline is projected during autumn. Nevertheless, the variations of wind resources in winter and spring are relatively insignificant. The outcomes of this study are expected to offer a framework for fine-scale wind resource assessment under climate change, and facilitate the economic and risk assessments of future wind farm projects. [ABSTRACT FROM AUTHOR]