Receding Interval Prediction of District Heat Load via Finite Difference Multi-Operating-Domain Dynamic Modelling

Abstract To realize low carbon emission, district heat load (DHL) prediction is of great significance to the efficient operation of urban district heating system (DHS). Due to spatial topology of central heating network, weather conditions and heat users show great spatiotemporal dispersion, whose influence to DHL is very complex and its prediction is challengeable. To solve this problem, a multi-operating-domain dynamic modelling structure is proposed in this paper. Firstly, through detailed analysis from first-principle, external factors such as weather and user factors are quantitatively included to form candidate inputs set where the NCA (Neighbourhood Component Analysis) algorithm is used to extract feature inputs. Considering time-delays of feature inputs to DHL, AIC (Akaike Information Criterion) is applied to determine delay orders. Then, feature inputs, DHL output and their delay-orders are integrated to form a finite difference operating space, representing by a regression vector. After its high-dimensional clustering and hyperplane estimation, the whole space is convex partitioned into multiple polyhedral domains. In each domain, using the finite difference input vector and output vector, multi-step receding prediction of heat load is realized using the Bi-LSTM (Bidirectional Long-short Temporal Memory) neural network. To further quantify prediction error, the nonparametric CKDE (Conditional Kernel Density Estimation) method are adopted to evaluate prediction uncertainty, where confidence boundaries can be obtained as interval prediction models. Finally, a DHS in Northwest China is selected for verification. The results show that the proposed method can accurately predict short-term DHL under the proposed multi-model structure. It is meaningful to achieve the purposes of on-demand heating, energy saving and environmental protection.