基于SVD-LSTM的高校学生宿舍空调负荷预测

doi:10.16180/j.cnki.issn1007-7820.2020.11.012

Abstract

Abstract:

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.

Key words: college load, air-conditioning load characteristics, SVD, LSTM, load forecasting, correlation coefficient

CLC Number:

TP183

QI Xin,WANG Fuzhong,ZHANG Li,WANG Rui,WANG Xiaohui. Air Conditioning Load Forecast of University Students' Dormitory Based on SVD-LSTM[J].Electronic Science and Technology, 2020, 33(11): 59-66.

Figures/Tables 12

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References 24

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楼层	寝室数目/间	占总寝室数比例	抽样寝室数目/间
1层	16	12%	3
2层	35	26%	7
3层	39	29%	7
4层	44	33%	9
总计	134	100%	26

隐藏层数	Block数目/个	迭代次数/次	每组输入数据数/个	RMSE	MAE
1	1	1 600	12	0.066 46	0.095 6
1	2	1 600	12	0.066 52	0.095 5
1	3	1 600	12	0.066 72	0.095 8
1	4	1 600	12	0.065 71	0.095 1
1	5	1 600	12	0.065 95	0.094 5

隐藏层数	Block数目/个	迭代次数/次	每组输入数据数/个	RMSE	MAE
1	4	1 600	12	0.065 71	0.095 1
2	4	1 600	12	0.066 51	0.095 3
3	4	1 600	12	0.065 31	0.094 9
4	4	1 600	12	0.065 57	0.095 3
5	4	1 600	12	0.067 08	0.094 9

模型	RMSE	MAE
ARIMA	0.105 00	0.113 3
LSTM	0.065 31	0.094 9
SVD-LSTM	0.064 37	0.093 3

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

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