冬冷夏热地区典型办公建筑微气象参数与空调冷负荷映射数据

本数据适用于冬冷夏热地区典型办公建筑。数据包括微气象参数（如温度、湿度、焓值、湿球温度等）空调预测冷负荷。基于已有历史数据集，利用神经网络，回归分析和时间序列模型，根据气象条件预测空调冷负荷。综合运用这些算法和数据，可以有效提升办公建筑的能源利用效率。基于日期、24小时时间标记、室外干球温度以及室外相对湿度等核心参数，系统首先通过时间智能算法自动辨识给定日期是否为法定工作日。随后，利用室外干球温度与室外相对湿度的实时数据，基于温湿度的焓值与湿球温度推导算法，计算出相应的空气焓值与湿球温度，这两个参数对于环境热舒适度的评估至关重要。
进一步地，系统依托丰富的历史数据集，运用先进的机器学习技术和回归分析手段，深入挖掘数据间的潜在关联。同时，结合时间序列分析模型，精准定位与当前日期最为接近的相似日，为后续的预测分析奠定坚实基础。
在此基础上，系统采用神经网络算法这一前沿技术，根据实时气象参数对空调冷负荷值进行高精度预测。这一预测过程不仅涵盖了新风负荷及其负荷率、设备负荷及其负荷率，还细致考虑了围护结构负荷及其负荷率，从而实现了对空调系统整体能耗的全方位、多层次评估。此外，系统还能够输出逐时负荷及其负荷率，为精细化管理和能效优化提供了强有力的数据支持。

This dataset is applicable to typical office buildings in hot-summer and cold-winter zones. The dataset includes micrometeorological parameters (such as temperature, humidity, enthalpy, wet-bulb temperature, etc.) and predicted air conditioning cooling loads. Based on existing historical datasets, neural networks, regression analysis and time series models are utilized to predict air conditioning cooling loads according to meteorological conditions. The integrated application of these algorithms and data can effectively improve the energy utilization efficiency of office buildings.
Based on core parameters including date, 24-hour time stamps, outdoor dry-bulb temperature and outdoor relative humidity, the system first automatically identifies whether a given date is a legal working day via intelligent time-based algorithms. Subsequently, leveraging real-time data of outdoor dry-bulb temperature and outdoor relative humidity, and based on the enthalpy and wet-bulb temperature derivation algorithm for temperature and humidity, the system calculates the corresponding air enthalpy and wet-bulb temperature, both of which are critical for the assessment of environmental thermal comfort.
Furthermore, relying on the rich historical dataset, the system adopts advanced machine learning techniques and regression analysis methods to deeply excavate the potential correlations within the data. Meanwhile, combined with time series analysis models, it accurately locates the most similar days closest to the current date, laying a solid foundation for subsequent predictive analysis.
On this basis, the system adopts cutting-edge technologies represented by neural network algorithms to conduct high-precision prediction of air conditioning cooling load values based on real-time meteorological parameters. This prediction process not only covers fresh air load and its load rate, equipment load and its load rate, but also fully considers envelope load and its load rate, thereby realizing an all-round and multi-level evaluation of the overall energy consumption of the air conditioning system. In addition, the system can also output hourly loads and their load rates, providing powerful data support for refined management and energy efficiency optimization.

搜集汇总

数据集介绍

特点

该数据集包含冬冷夏热地区典型办公建筑的微气象参数和空调冷负荷数据，适用于能源效率优化和负荷预测。