Cooling load prediction through recurrent neural networks

Abstract

In this paper, we focus on recurrent neural networks and investigate their applicability to some identification or prediction problems. After reviewing the well-known learning algorithms for recurrent neural networks, called back propagation through time (BPTT) and real-time recurrent learning (RTRL), we investigate their performance when applied to relatively small-scale problems and evaluate the computational complexity of them. Following these investigations, we apply the recurrent neural networks to large-scale cooling load prediction problems in a district heating and cooling system. However, the computational complexity is enormous and learning within practical time seems to be very difficult. For decreasing such difficulties, we propose a model that preserves output values observed within an appropriate period. Through a lot of numerical simulations, it is shown that the proposed model has an ability to learn long cycle time series within relatively short time. >