基于SVD-LSTM的高校学生宿舍空调负荷预测

准确预测高校空调负荷是保证高校安全用电和电力高峰期区域配电网稳定运行的前提。文中以高校空调负荷中具有代表性的学生宿舍空调负荷为对象,建立了基于奇异值分解-长短期记忆网络的高校学生宿舍空调负荷预测模型。该方法以高校学生宿舍空调负荷特性为基础,使用奇异值分解进行降噪处理,通过长短期记忆网络对高校学生宿舍空调负荷进行预测。文中以武汉某高校的真实数据为样本进行了分析验证,通过与传统预测模型对比,证明所提预测模型的预测效果和精度优于传统预测方法。

Air Conditioning Load Forecast of University Students' Dormitory Based on SVD-LSTM

Accurate prediction of air conditioning load in colleges is the premise and basis to ensure the safe electricity consumption and stable operation of regional distribution network during power peak period. In this paper, the student dormitory air conditioning load of college air conditioning is taken as the research object, and an air conditioning load forecasting model based on SVD-LSTM is established. Based on the characteristics of air conditioning load in college dormitory, this model uses SVD to reduce data noise, and predicts the air conditioning load of college students' dormitory through LSTM. The actual data of a university in Wuhan is taken as a sample to analyze and verify the model. It is proves that the prediction result of SVD-LSTM is better by the comparison with traditional prediction model results. The model improves the prediction accuracy. The analysis of a university in Wuhan shows that the prediction effect and accuracy of the proposed prediction model are better than the traditional prediction method.