ABSTRACT

Fluid temperature predictions of geothermal borefields usually involve temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modelled as a linear state-space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history to obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1${}^{\circ }$C and 1${}^{\circ }$C, respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile estimates at the expense of about five times more computational time.

摘要

地热钻孔场的流体温度预测通常涉及其特征g函数的时间叠加，使用负荷聚合方案来减少计算时间。假设地面具有线性特性，则可以将其建模为线性状态空间系统，其中状态是合计载荷。但是，当井筒已经在运行且未记录其载荷历史记录或数据中存在间隙时，这些模型的应用和准确性会受到影响。本文评估了状态观测器的性能，以估计井场载荷历史以获得准确的流体预测。结果表明，时变卡尔曼滤波器（TVKF）和运动水平估计器（MHE）均提供了平均误差和最大误差低于0.1的预测${}^{\circ }$C和1${}^{\circ }$C分别。在前n步输出预测和负载历史曲线估计方面，MHE优于TVKF，但要花费大约五倍的计算时间。