Energy consumption prediction of aircraft ground air conditioning based on ISCA-DBN

An improved sine-cosine optimization (ISCA) deep belief network (DBN) prediction model for ground air conditioning energy consumption is suggested in order to increase the prediction accuracy of ground air conditioning energy consumption when the aircraft cabin is cooled by ground air conditioning. In contrast to the standard sine-cosine optimization algorithm, the improved sine-cosine algorithm introduces a cosine adjustment factor to create a new non-linear oscillation adjustment factor to balance the algorithm's overall performance. It also suggests an improved logistic chaotic map, which increases population diversity. In order to prevent the algorithm from reaching a local optimum, a learning technique based on the concept of mutation evolution is finally suggested.Search and local optimization capabilities; finally, a learning strategy is proposed based on the idea of mutation evolution to avoid the algorithm from falling into local optimum. The ISCA-DBN model is applied to the prediction of ground air-conditioning energy consumption of Boeing 737-800 aircraft, and the performance is compared with back propagation (BP)、support vector machine (SVM)、DBN algorithms. There is a certain improvement in both prediction accuracy and real-time performance.