Natural ventilation is driven by either buoyancy forces or wind pressure forces or their combinations that inherit stochastic variation into ventilation rates. Since the ventilation rate is a nonlinear function of mul...Natural ventilation is driven by either buoyancy forces or wind pressure forces or their combinations that inherit stochastic variation into ventilation rates. Since the ventilation rate is a nonlinear function of multiple variable factors including wind speed, wind direction, internal heat source and building structural thermal mass, the conventional methods for quantifying ventilation rate simply using dominant wind direction and average wind speed may not accurately describe the characteristic performance of natural ventilation. From a new point of view, the natural ventilation performance of a single room building under fluctuating wind speed condition using the Monte-Carlo simulation approach was investigated by incorporating building facade thermal mass effect. Given a same hourly turbulence intensity distribution, the wind speeds with 1 rain frequency fluctuations were generated using a stochastic model, the modified GARCH model. Comparisons of natural ventilation profiles, effective ventilation rates, and air conditioning electricity use for a three-month period show statistically significant differences （for 80% confidence interval） between the new calculations and the traditional methods based on hourly average wind speed.展开更多
针对风电场规划中风速的高随机性问题,提出了一种基于小样本空时融合压缩残差网络点预测(spatio-temporal integration and compression deep residual,STiCDRS)模型。该模型旨在深入挖掘风速序列中的空间和时间特征,以提升点预测精度...针对风电场规划中风速的高随机性问题,提出了一种基于小样本空时融合压缩残差网络点预测(spatio-temporal integration and compression deep residual,STiCDRS)模型。该模型旨在深入挖掘风速序列中的空间和时间特征,以提升点预测精度。首先,采用空时融合压缩残差网络点预测模型得到点预测结果。然后,在此基础上采用新颖的空时融合压缩残差网络区间(STiCDRS-Gaussian process regression,STiCDRS-GPR)预测模型得到风速的区间预测结果,进而得到更为可靠的风速概率预测结果。该模型采用贝叶斯优化方法进行超参数选择,确保超参数的高效自动化调优。最后,使用内蒙古地区风电场的风速数据集,将STiCDRS模型与传统经典模型的预测结果进行对比。实验结果表明,相较于其他模型,所提STiCDRS-GPR模型在风速预测中具有更高的点预测精度、适宜的预测区间以及可靠的概率预测结果,充分展示了其在风速预测领域的良好应用潜力。展开更多
文摘Natural ventilation is driven by either buoyancy forces or wind pressure forces or their combinations that inherit stochastic variation into ventilation rates. Since the ventilation rate is a nonlinear function of multiple variable factors including wind speed, wind direction, internal heat source and building structural thermal mass, the conventional methods for quantifying ventilation rate simply using dominant wind direction and average wind speed may not accurately describe the characteristic performance of natural ventilation. From a new point of view, the natural ventilation performance of a single room building under fluctuating wind speed condition using the Monte-Carlo simulation approach was investigated by incorporating building facade thermal mass effect. Given a same hourly turbulence intensity distribution, the wind speeds with 1 rain frequency fluctuations were generated using a stochastic model, the modified GARCH model. Comparisons of natural ventilation profiles, effective ventilation rates, and air conditioning electricity use for a three-month period show statistically significant differences （for 80% confidence interval） between the new calculations and the traditional methods based on hourly average wind speed.
文摘针对风电场规划中风速的高随机性问题,提出了一种基于小样本空时融合压缩残差网络点预测(spatio-temporal integration and compression deep residual,STiCDRS)模型。该模型旨在深入挖掘风速序列中的空间和时间特征,以提升点预测精度。首先,采用空时融合压缩残差网络点预测模型得到点预测结果。然后,在此基础上采用新颖的空时融合压缩残差网络区间(STiCDRS-Gaussian process regression,STiCDRS-GPR)预测模型得到风速的区间预测结果,进而得到更为可靠的风速概率预测结果。该模型采用贝叶斯优化方法进行超参数选择,确保超参数的高效自动化调优。最后,使用内蒙古地区风电场的风速数据集,将STiCDRS模型与传统经典模型的预测结果进行对比。实验结果表明,相较于其他模型,所提STiCDRS-GPR模型在风速预测中具有更高的点预测精度、适宜的预测区间以及可靠的概率预测结果,充分展示了其在风速预测领域的良好应用潜力。
