基于C-LSTM的短期用电预测模型和应用

doi:10.6040/j.issn.1672-3961.0.2020.226

摘要/Abstract

摘要： 基于深度学习下的长短期记忆循环神经网络对家庭短期用电预测进行研究。本研究引入卷积神经网络(convolutional neural network, CNN)和长短期记忆(long short term memory, LSTM)模型结合的混合深度神经网络模型C-LSTM,并在此模型基础上提出多步预测策略。根据对5个真实家庭日常用电数据集的研究,C-LSTM实现了以5 min为单位的家庭电力需求预测。通过不断修改模型参数、完善模型,从本研究提供的3种误差指标的分析来看,C-LSTM预测准确性高于自回归集成移动平均模型、支持向量回归模型和LSTM模型。本研究评价模型预测效果的主要依据是平均绝对百分比误差值(mean absolute percentage error, MAPE), 从试验结果可得C-LSTM 模型在5 min的家庭需求电力预测,比支持向量回归模型提升4.63%,比 LSTM提升22.8%,比自回归集成移动平均模型提升 34.74%。因此,C-LSTM模型为智能电网对家庭层面电需求的准确及时预测提供了保障,对推动个性化用电套餐的广泛普及、减少能源浪费产生重要影响。

关键词: 短期家庭用电预测, 多步预测, 卷积神经网络, 长短期记忆模型, 混合深度学习神经网络

Abstract: Research on short-term household electricity consumption prediction based on long-term short-term memory recurrent neural network under deep learning. This research introduced a hybrid deep neural network model C-LSTM that combines convolutional neural network(CNN)and long short term memory(LSTM)models, and proposed a multi-step prediction strategy based on this model. According to the research on the daily electricity consumption data set of 5 real households, C-LSTM realized household electricity demand forecasting in 5 min. Through continuous modification of model parameters and improvement of the model, from the analysis of the three error indicators provided in this study, the prediction accuracy of C-LSTM was higher than the autoregressive integrated moving average model, support vector regression model and LSTM model. The main basis for the evaluation of the model prediction effect in this study was the average absolute percentage error value. From the test results, it could be obtained that the C-LSTM model's household electricity demand forecast in 5 minutes was 4.63% higher than the support vector regression model, 22.8% higher than the LSTM, and 34.74% higher than the autoregressive integrated moving average model. Therefore, the C-LSTM model provided a guarantee for the smart grid's accurate and timely prediction of household-level electricity demand, and had an important impact on promoting the widespread popularity of personalized electricity packages and reducing energy waste.

Key words: short-term household electricity forecast, multi-step forecasting, convolutional neural network, long short term memory model, hybrid deep learning neural network

中图分类号:

TP18

廖锦萍,莫毓昌,YAN Ke. 基于C-LSTM的短期用电预测模型和应用[J]. 山东大学学报 (工学版), 2021, 51(2): 90-97.

LIAO Jinping, MO Yuchang, YAN Ke. Model and application of short-term electricity consumption forecast based on C-LSTM[J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 90-97.

参考文献

[1] 何永秀,王跃锦,杨丽芳,等. 基于最小二乘支持向量机的居民用电预测研究[J]. 电力需求侧管理, 2010, 12(3): 19-23. HE Yongxiu, WANG Yuejin, YANG Lifang, et al. Research on residential electricity prediction based on the least squares support vector machine[J]. Power Demand Side Management, 2010, 12(3): 19-23.
[2] GYAMFIFI S, KRUMDIECK S. Scenario analysis of residential demand response at network peak periods[J]. Electric Power System Research, 2012, 93: 32-38.
[3] KONG Weicong, DONG Zhaoyang, HILL D J, et al. Short-term residential load forecasting based on resident behavior learning[J]. IEEE Transactions on Power Systems, 2018, 33(1): 1087-1088.
[4] CHEN Sijie, LIU Chenching. From demand response to transactive energy: State of the art[J]. Journal of Modern Power Systems and Clean Energy, 2017, 5: 10-19.
[5] GOUNTIS V P, BAKIRTZIS A G. Bidding strategies for electricity producers in a competitive electricity mark-etplace[J]. IEEE Transaction Power Systems, 2004, 19(1): 356-365.
[6] KIAN A R, Jr CRUZ J B. Bidding strategies in dynamic electricity markets[J]. Decision Support Systems, 2005, 40(3/4): 543-551.
[7] SIANO P. Demand response and smart grids: a survey[J]. Renewable and Sustainable Energy Reviews, 2014, 30: 461-478.
[8] HU Min, JI Zhiwei, YAN Ke, et al. Detecting anomalies in time series data via a meta-feature based approach[J]. IEEE Access, 2018, 6: 27760-27776.
[9] YUAN Chaoqing, LIU Sifeng, FANG Zhigeng. Comparison of China's primary energy consumption forecasting by using ARIMA(the autoregressive integrated moving average)model and GM(1, 1)model[J]. Energy, 2016, 100: 384-390.
[10] YAN Ke, DU Yang, REN Zixiao. MPPT perturbation optimization of photovoltaic power systems based on solar irradiance data classification[J]. IEEE Transactions on Sustainable Energy, 2019, 10(2): 514-521.
[11] DU Yang, YAN Ke, REN Zixiao, et al. Designing localized MPPT for PV systems using fuzzy-weighted extreme learning machine[J]. Energies, 2018, 11(10): 2615.
[12] CHEN Xiaoyang, DU Yang, WEN Huiqing, et al. Forecasting based power ramp-rate control strategies for utility-scale PV systems[J]. IEEE Transactions on Industrial Electronics, 2019, 66(3): 1862-1871.
[13] KARNIK N N, MENDEL J M. Applications of type-2 fuzzy logic systems to forecasting of time-series[J]. Information Sciences, 1999(1/2/3/4), 120: 89-111.
[14] CONEJO A J, PLAZAS M A, ESPINOLA R, et al. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models[J]. IEEE Transactions on Power Systems, 2005, 20(2): 1035-1042.
[15] YAN Ke, JI Zhiwei, SHEN Wen. Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM[J]. Neurocomputing, 2017, 228: 205-212.
[16] YAN Ke, SHEN Wen, MULUMBA T, et al. ARX model based fault detection and diagnosis for chillers using support vector machines[J]. Energy and Buildings, 2014, 81: 287-295.
[17] KUMAR U, JAIN V. Time series models(Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis)to forecast energy consumption in India[J]. Energy, 2010, 35(4): 1709-1716.
[18] EDIGER V S, AKAR S. ARIMA forecasting of primary energy demand by fuel in turkey[J]. Energy Policy, 2007, 35(3): 1701-1708.
[19] OGCU G, DEMIREL O F, ZAIM S. Forecasting electricity consumption with neural networks and support vector regression[J]. Procedia-Social and Behavioral Sciences, 2012, 58: 1576-1585.
[20] RODRIGUES F, CARDEIRA C, CALADO J M F. The daily and hourly energy consumption and load fore-casting using artificial neural network method: a case study using a set of 93 households in Portuga[J]. Energy Procedia, 2014, 62: 220-229.
[21] DEB C, EANG L S, YANG J, et al. Forecasting energy consumption of institutional buildings in Singapore[J]. Procedia Engineering, 2015, 121: 1734-1740.
[22] WANG Jianzhou, HU Jianming. A robust combination approach for short-term wind speed forecasting and analysis: combination of the ARIMA(autoregressive integrated moving average), ELM(extreme learning machine), SVM(support vector machine)and LSSVM(least square SVM)forecasts using a GPR(gaussian process regression)model[J]. Energy, 2015, 93: 41-56.
[23] ARMANO G, MARCHESI M, MURRU A. A hybrid genetic-neural architecture for stock indexes forecasting[J]. Information Sciences, 2005, 170(1): 3-33.
[24] RATHER A M, AGARWAL A, SASTRY V. Recurrent neural network and a hybrid model for prediction of stock returns[J]. Expert Systems with Applications, 2015, 42(6): 3234-3241.
[25] WANG H Z, WANG G B, LI G Q, et al. Deep belief network based deterministic and probabilistic wind speed forecasting approach[J]. Applied Energy, 2016, 182: 80-93.
[26] KHODAYAR M, KAYNAK O, KHODAYAR M E. Rough deep neural architecture for short-term wind speed forecasting[J]. IEEE Transactions on Industrial Informatics, 2017, 13(6): 2770-2779.
[27] VOYANT C, NOTTON G, KALOGIROU S, et al. Machine learning methods for solar radiation forecasting: a review[J]. Renewable Energy, 2017, 105: 569-582.
[28] ALZAHRANI A, SHAMSI P, DAGLI C, et al. Solar irradiance forecasting using deep neural networks[J]. Procedia Computer Sciences, 2017, 114: 304-313.
[29] ALMALAQ A, EDWARDS G. A Review of deep learning methods applied on load forecasting[C] //Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications(ICMLA). Cancun, Mexico: IEEE, 2017.
[30] SHI Heng, XU Minhao, LI Ran. Deep learning for household load forecasting: a novel pooling deep RNN[J]. IEEE Transactions on Smart Grid, 2018, 9(5): 5271-5280.
[31] KONG Weicong, DONG Zhaoyang, JIA Youwei, et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 2019, 10(1): 841-851.
[32] KELLY J, KNOTTENBELT W. The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes[J]. Scientific Data, 2015, 2.
[33] KETKAR N. Convolutional neural networks in deep learning with python[M]. Berlin, Germany: Springer, 2017: 63-78.
[34] JOZEFOWICZ R, ZAREMBA W, SUTSKEVER I. An empirical exploration of recurrent network architectures[C] //Proceedings of the 32nd International Conference on International Conference on Machine Learning. Lille, France: ACM, 2015.
[35] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computer, 1997, 9(8): 1735-1780.
[36] WERBOS P J. Backpropagation through time: what it does and how to do it[J]. Proceeding of IEEE, 1990, 78(10): 1550-1560.
[37] BRAILSFORD T J, FAFF R W. An evaluation of volatility forecasting techniques[J]. Journal of Banking & Finance, 1996, 20(3): 419-438.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed