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Configuration of a genetic algorithm for multi-objective optimisation of solar gain to buildings.

, , and . GECCO, page 1327-1328. ACM, (2010)

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Configuration of a genetic algorithm for multi-objective optimisation of solar gain to buildings.. GECCO (Companion), page 2003-2006. ACM, (2010)On the Joint Control of Multiple Building Systems with Reinforcement Learning., , , and . e-Energy, page 60-72. ACM, (2021)Comparing Gray Box Methods to Derive Building Properties from Smart Thermostat Data., , , and . BuildSys@SenSys, page 223-232. ACM, (2019)besos: Building and Energy Simulation, Optimization and Surrogate Modelling., , , , and . J. Open Source Softw., 6 (60): 2677 (2021)Using simple predictive models to improve control of complex building systems., and . BuildSys@SenSys, page 26:1-26:4. ACM, (2017)Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models., and . CoRR, (2020)Clustering and Ranking Based Methods for Selecting Tuned Search Heuristic Parameters., , and . CEC, page 2931-2940. IEEE, (2019)Reducing Computation Time with a Rolling Horizon Approach Applied to a MILP Formulation of Multiple Urban Energy Hub System., , and . ICCS, volume 51 of Procedia Computer Science, page 2137-2146. Elsevier, (2015)Facilitating Model Reuse and Integration in an Urban Energy Simulation Platform., and . ICCS, volume 51 of Procedia Computer Science, page 2127-2136. Elsevier, (2015)BESOS: a Collaborative Building and Energy Simulation Platform., , , and . BuildSys@SenSys, page 350-351. ACM, (2019)