Search

• Propose a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of cooling load and outdoor wet-bulb temperature. • Obtain the optimal match of prediction models and optimization algorithms for improving the performance of the MLB-MPC. • Investigate the impact of disturbances on the performance of global optimization. • Achieve an energy-saving ratio of 7.1% for the air-conditioning system when compared with the measured operation data. The dynamic optimization of key setpoints (e.g., supply water temperatures of chillers and cooling towers, or indoor temperature and humidity) can track the efficient performance point of the air-conditioning system (ACS), thus obtaining a great energy-saving effect. The model predictive control is an effective way to precisely adjust these setpoints. However, most of the existing studies focused on simple linear strategies, e.g., experience-based, rule-based, or component-based, which results in unsatisfactory energy efficiency. Aiming at this, the paper proposed a novel machine learning-based model predictive control (MLB-MPC) framework with predictive inputs of disturbances. The framework first employed the grid search and cross-validation to optimize the total energy consumption prediction models (TECPMs), which were built by data-driven models (e.g., multiple linear regression, artificial neural network, support vector regression (SVR), and random forest). And then the optimization performances of each TECPM to the controlled variables were obtained by using different optimization algorithms (e.g., genetic algorithm, particle swarm optimization (PSO), and simulated annealing). Finally, the optimal match of TECPMs and optimization algorithms was achieved by trade-off among prediction accuracies, optimization accuracies, and optimization time. The case studies demonstrated that the optimal match for the MLB-MPC is SVR and PSO as they had the highest prediction accuracy (mean absolute percentage error of 2.5%) and shortest optimization time (41 ms/per) and optimization accuracies with little difference. After adopting the optimal match, the MLB-MPC with predictive disturbing inputs achieved a great energy-saving ratio of 7.1% for the ACS, which was only less than the MLB-MPC with ideal disturbing inputs by 0.4%. This indicated that exact predictions of disturbances (e.g., cooling load and outdoor wet-bulb temperature) are important for the MLB-MPC. The proposed MLB-MPC framework would provide a method for improving the energy efficiency of ACSs. [ABSTRACT FROM AUTHOR]

In order to conduct in-depth researches on rationality of air conditioning system equipment of a high-speed train as well as its pipeline system design, working conditions of the air conditioning system and distribution of aerodynamic characteristics including pressure, velocity and temperature in high speed trains should be computed carefully at the design stage. Therefore, the finite volume method was used to solve a governing equation of computational fluid dynamics. The aerodynamic characteristics of pipelines of the air conditioning system and the complete high speed train were computed, so the indoor distribution of wind velocity, temperature and gas concentration (carbon oxide, carbon dioxide, nitrogen and so forth) was obtained. The flow field index and the thermal comfort index were used to evaluate the indoor thermal comfort degree. In this way, whether rationality of the air conditioning system design and indoor aerodynamic characteristic could satisfy requirements for design specifications can be analyzed. Results show that: Under winter or summer working conditions, wind velocity was relatively high at the passageway door, where the maximum wind velocity was more than 1 m/s and would make passengers uncomfortable. Air flow velocity outside comfortable regions was more than 0.05 m/s, satisfying UIC553 standards. Velocity distribution was basically the same indoors, where wind velocity was large at positions perpendicular to the air supply hole, and the maximum wind velocity was more than 1 m/s, but wind velocity was uniform in the passenger region, which was basically lower than 0.2 m/s and satisfied UIC553 standards. In summer and winter working conditions, distribution of pressure, velocity and temperature was not uniform, where the maximum temperature gradient was near the air inlet in the compartment. Air components in the compartment satisfied requirements for comfort. The most uncomfortable regions in the compartment were concentrated at the passageway. Air supply holes distributed symmetrically on the train roof caused high air flow intensity, high wind velocity, low temperature and high humidity at the passageway in the compartment, so the thermal comfort was low, and it is feasible to adjust the layout of air supply holes appropriately. [ABSTRACT FROM AUTHOR]