Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data. Traditionally, most studies utilize airport weather information as the decision inputs. However, most buildings are in environments that are quite different than those at the airport miles away. Tree cover, adjacent buildings, and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature, humidity, solar irradiance, and wind that are large enough to influence design and operation decisions. In order to overcome this challenge, there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales. This paper reviews and complies knowledge on common weather data resources, data processing methodologies and forecasting techniques of weather information. Commonly used statistical, machine learning and physical-based models are discussed and presented as two major categories: deterministic forecasting and probabilistic forecasting. Finally, evaluation metrics for forecasting errors are listed and discussed.

用于新建筑和翻新的高级建筑控制和能源优化依赖于准确的天气数据。传统上，大多数研究都将机场天气信息用作决策输入。但是，大多数建筑物所处的环境与机场相距几英里的环境完全不同。树木覆盖物，相邻建筑物以及较大的周围区域所引起的微气候效应都可能导致空气温度，湿度，太阳辐射和风的偏差大到足以影响设计和操作决策的程度。为了克服这一挑战，已有许多先前的研究在开发从微尺度到中尺度的天气预报算法。本文回顾并整理了有关常见天气数据资源，数据处理方法和天气信息预报技术的知识。讨论并介绍了常用的统计，机器学习和基于物理的模型，将其分为两个主要类别：确定性预测和概率性预测。最后，列出并讨论了预测误差的评估指标。