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Understanding the reliability of localized near future weather data for building performance prediction in the UK

机译:了解近期未来天气数据的可靠性,以便在英国建立性能预测

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Access to reliable site-specific near future weather data is crucial for forecasting temporally-dynamic building energy demand and consumption, and determining the state of on-site renewable energy generation. Often there is a missing link between weather forecast providers and building energy management systems. This short paper discusses the potential to conduct building performance modelling using localized high resolution weather forecast freely available from the United Kingdom Met Office DataPoint service. It creates a great opportunity for building performance simulation professionals and building energy managers to re-use site-specific high resolution weather forecast data to predict near future building performance at both individual building and city scale. In this paper, authors have developed a framework of forecasting near future building performance and a Matlab script to automatically gather observed weather data from 140 weather stations and weather forecasts for nearly 6,000 locations in the UK. To understand the reliability of weather forecast, three-hourly forecasts of temperature, relative humidity, wind speed and wind direction are compared with observations from weather stations. This provides evidences to use the next 24-hour forecast to predict dynamic building energy demand and consumption, and determine the on-site renewable energy generation output. Because of the high accuracy of forecast, the rolling forecast can be recorded on daily basis to construct weather files for locations that do not have weather stations. This will increase current 14 locations of the CIBSE weather data to nearly 6,000 locations covering population centers, sporting venues and tourist attractions.
机译:访问可靠的现场特定于未来的天气数据对于预测时间 - 动态建筑能源需求和消费以及确定现场可再生能源的状态至关重要。通常存在天气预报提供商和建筑能源管理系统之间的缺失联系。本文简介讨论了采用局部高分辨率天气预报进行建筑绩效建模的潜力,从英国遇到Office DataPoint Service免费提供。它为建立绩效模拟专业人员和建立能源管理人员来重新使用特定于站点的高分辨率天气预报数据来预测个人建筑和城市规模的未来建筑业绩的绝佳机会。在本文中,作者制定了一个在未来的建筑物绩效附近预测的框架,而Matlab脚本将自动收集来自140个气象站和天气预报的观测到的天气数据,在英国近6000个地点。为了了解天气预报的可靠性,将三小时的温度预测,相对湿度,风速和风向与天气站的观察结果进行比较。这提供了利用接下来的24小时预测来预测动态建筑能源需求和消耗,并确定现场可再生能源产量的证据。由于预测的高精度,可以在日常记录滚动预测以构建没有气象站的地方的天气文件。这将使Cibse天气数据的当前14个位置增加到覆盖人口中心,体育场馆和旅游景点的近6000个地点。

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