Two neural-metaheuristic techniques based on vortex search and backtracking search algorithms for predicting the heating load of residential buildings

Analyzing the thermal load is a significant task for energy-efficient buildings. Intelligent models have shown high reliability for predicting the heating load of different buildings. But computational shortcomings like local minima have yet remained a drawback that can be remedied by optimization techniques. This study investigates the efficiency of two novel metaheuristic algorithms, namely vortex search algorithms (VSA) and backtracking search algorithm (BSA), for optimizing the performance of a multilayer perceptron neural network (MLP). The methods are used to predict the heating load of a residential building. Evaluation of the results revealed that the proposed metaheuristic schemes could properly optimize the MLP. In this regard, the training error experienced around 19.99 and 5.99% reduction by synthesizing the VSA and BSA, respectively. These values were obtained 20.39 and 6.18% for the testing phase. Also, the correlation of the MLP products rose from 93.52 to 95.62 and 94.00%. Although the best-fitted BSA was around six times as fast as VSA, the VSA-base ensemble enjoys more accuracy of prediction. Overall, the findings showed that utilizing the VSA-MLP and BSA-MLP models is a promising way for the early prediction of the heating load.