BURA Collection: http://bura.brunel.ac.uk/handle/2438/13039 2026-04-07T05:22:23Z 2026-04-07T05:22:23Z CLOSED-LOOP GEOTHERMAL SYSTEMS: PARAMETRIC STUDY INCLUDING INTERMITTENT OPERATION Draper, P Kubacka, J Seymour, K Karayiannis, T http://bura.brunel.ac.uk/handle/2438/33097 2026-04-03T02:00:46Z 2026-01-01T00:00:00Z Title: CLOSED-LOOP GEOTHERMAL SYSTEMS: PARAMETRIC STUDY INCLUDING INTERMITTENT OPERATION Authors: Draper, P; Kubacka, J; Seymour, K; Karayiannis, T Abstract: ... 2026-01-01T00:00:00Z FLOW BOILING PRESSURE DROP IN A SINGLE MICROCHANNEL AND COMPARISON WITH CORRELATIONS Widgington, JJ Ivanov, A Karayiannis, T http://bura.brunel.ac.uk/handle/2438/33096 2026-04-03T02:00:45Z 2026-01-01T00:00:00Z Title: FLOW BOILING PRESSURE DROP IN A SINGLE MICROCHANNEL AND COMPARISON WITH CORRELATIONS Authors: Widgington, JJ; Ivanov, A; Karayiannis, T Abstract: ... Description: ... 2026-01-01T00:00:00Z Enhancing Wind Energy Forecasting Efficiency Through Dense and Dropout Networks (DDN): Leveraging Grid Search Optimization Alazemi, T Darwish, M Alaraj, M Alsisi, E http://bura.brunel.ac.uk/handle/2438/32991 2026-03-17T03:01:02Z 2025-11-16T00:00:00Z Title: Enhancing Wind Energy Forecasting Efficiency Through Dense and Dropout Networks (DDN): Leveraging Grid Search Optimization Authors: Alazemi, T; Darwish, M; Alaraj, M; Alsisi, E Editors: Arai, K Abstract: The wind power industry has experienced remarkable growth due to technological advancements and innovative business models. In 2020, the global installed wind power capacity reached 93 GW, marking a significant 52.96% increase compared to the previous year. This growth highlights the industry’s pivotal role in addressing energy needs and sustainability challenges. Timely wind energy forecasting is critical due to the nonlinear relationship between wind speed and power generation—however, the complexity and uncertainty of natural wind factors present challenges, necessitating effective forecasting methods. A deep learning-based approach named Dense and Dropout Networks (DDN) is introduced to address these challenges, employing Grid Search Optimization techniques. The model consists of eight dense layers for intricate data pattern recognition and a “ReLU” activation function. A dropout layer with a rate of 0.4 is integrated to enhance generalization and mitigate overfitting. The optimization process combines grid search with cross-validation to determine optimal hyperparameters. The actual “Texas Turbine” dataset evaluates the proposed DDN model based on Mean Squared Error (MSE) and Mean Absolute Error (MAE), revealing a significant improvement in accuracy with an enhanced MSE of 94.013% and an improved MAE of 76.947%. In conclusion, the optimized DDN model is a valuable and reliable tool for forecasting wind turbine energy production. Its impressive accuracy and potential for real-world implementation make it a noteworthy contribution to advancing renewable energy technologies and sustainable practices. 2025-11-16T00:00:00Z OPTIMIZING HYPERPARAMETER TUNING IN MACHINE LEARNING MODELS FOR FLOW BOILING IN MICROTUBES WITH BAYESIAN INFORMATION CRITERION Nazemzadeh, N Karayiannis, T Coletti, F http://bura.brunel.ac.uk/handle/2438/32894 2026-03-01T12:18:07Z 2026-08-02T00:00:00Z Title: OPTIMIZING HYPERPARAMETER TUNING IN MACHINE LEARNING MODELS FOR FLOW BOILING IN MICROTUBES WITH BAYESIAN INFORMATION CRITERION Authors: Nazemzadeh, N; Karayiannis, T; Coletti, F Abstract: ... 2026-08-02T00:00:00Z