基于神经网络的工厂用电状态识别方法

Neural Networks-Based Factory Power Consumption State Recognition

智能电表的迅速普及与应用引起电力消耗数据(即智能电网数据)的激增,这不仅给数据的存储与通信带来了挑战,同时也增加了对数据进行分析的难度。另外,由于生产性质和经营方式的不同,工厂的用电状态往往比较复杂。基于传统经验的人工识别不能满足实际应用的需求,该文研究了一种基于神经网络的工厂用电状态自动识别方法。首先,对采集于真实应用场景的电网大数据进行必要的预处理,包括数据的合并、清洗、标准化、打标和抽样;然后,基于预处理的电网数据构建神经网络模型用于对工厂用电状态的自动识别;最后,对提出的基于神经网络的工厂用电状态识别方法进行实验验证,证实了该方法的合理性和有效性。通过对工厂用电状态的准确识别,能够帮助供电公司指导企业进行错峰用电,进而有效缓解电力供给不平衡的问题,以达到对电能合理开发和利用的目的。

With the rapid application of smart electric meters,a large amount of power consumption data is generated,which challenges the data storage and communication.Meanwhile,due to the extensive production properties and business modes,the power consumption states of factories are complicated,which makes data analysis difficult.In this paper,a new neural networks-based power consumption state recognition method is proposed.The main efforts include three aspects:the pre-processing of power data,the modeling of neural network to automatically recognize the power consumption states of factories and the performance testing of the proposed method.The experimental results demonstrate the rationality and effectiveness of our method.The correct recognition of power consumption states of factories is helpful to the power supply company to arrange the sensible power supply strategy and efficient power utilization plan.