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Katayoon Kiany, Abolfazl Baghbani, Hossam Abuel-Naga, Hasan Baghbani, Mahyar Arabani and Mohammad Mahdi Shalchian
This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ulti...
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Jiaqi Yu, Wen-Shao Chang and Yu Dong
Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the mod...
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Emmanuel Okiria, Hiromu Okazawa, Keigo Noda, Yukimitsu Kobayashi, Shinji Suzuki and Yuri Yamazaki
The prediction of hydrological phenomena using simpler hydrological models requires less computing power and input data compared to the more complex models. Ordinarily, a more complex, white-box model would be expected to have better predictive capabilit...
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Emmanuel Pintelas, Ioannis E. Livieris and Panagiotis Pintelas
Machine learning has emerged as a key factor in many technological and scientific advances and applications. Much research has been devoted to developing high performance machine learning models, which are able to make very accurate predictions and decis...
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Rajan Gill and Raffaello D?Andrea
Two low-order, parametric models are developed for the forces and moments that a rotating propeller undergoes in forward flight. The models are derived using a first-principles-based approach, and are computationally efficient in the sense of being repre...
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Ludovico Danza, Lorenzo Belussi, Italo Meroni, Michele Mililli and Francesco Salamone
The paper describes an hourly simplified model for the evaluation of the energy performance of heat pumps in cooling mode maintaining a high accuracy and low computational cost. This approach differs from the methods used for the assessment of the overal...
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