基于预测技术的基站太阳能高效利用
2022-07-25 作者:熊勇,刘明明,胡先红   阅读量:
基于预测技术的基站太阳能高效利用 - 中兴通讯技术
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基于预测技术的基站太阳能高效利用
发布时间:2022-07-25  作者:熊勇,刘明明,胡先红  阅读量:

基于预测技术的基站太阳能高效利用

熊勇,刘明明,胡先红
(中兴通讯股份有限公司,中国 深圳 518057)

摘要:在日照资源充足的地区,大量通信基站配置了太阳能板和光伏充电模块,通过优先利用太阳能来减少市电电费开支。但是太阳能有较大的随机性和不确定性,实际中难以被充分利用。针对这个问题,提出了一种基于神经网络的方法。该方法通过实时采集的光照强度、温度和负载功率,结合天气预报、历史同期数据等,并借助神经网络的暴力计算,实现了太阳能产能和负载用能的预测;以高循环性能锂电池作为储能调用,实现了太阳能的主动、高效利用,减少了太阳能浪费及电费开支。  
关键词:太阳能;铁锂电池;神经网络;预测  


A Solar Energy Efficient Utilization Method for Communication Base Station Based on Prediction Technology

XIONG Yong, LIU Mingming, HU Xianhong
(ZTE Corporation, Shenzhen 518057, China)

Abstract: In the areas with abundant natural resources in the sunshine, a large number of communication base stations are equipped with solar panels and photovoltaic charging modules, so as to reduce the electricity cost by giving priority to the use of solar energy. However, solar energy has great randomness and uncertainty, so it is difficult to make full use of it in practice. In order to solve this problem, a method based on a neural network is proposed. Through the real-time collection of light intensity, temperature, and load power, combined with the weather forecast, and historical data of the same period, the prediction of solar energy production capacity and load energy consumption is realized through the violent calculation of a neural network. The high cycle performance lithium battery is used as energy storage to realize the active and efficient utilization of solar energy, reducing the waste of solar energy and electricity expenses.  
Keywords: solar energy; lithium-iron battery; neural network; prediction

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