Deep Learning Forecasting Based on Auto-LSTM Model for Home Solar Power Systems

The Internet of things is widely used to provide a lot of useful services such as human health care, security systems and green energy monitoring. It contributes to the sustainable development of smart cities in order to manage and integrate renewable energies. The swift growth of Home Solar Power Systems (HSPS) has enabled a large-scale collection of time series data. As advanced tools, smart meters can ensure the timely reading of HSPS data, automating metering and producing fine-grained data. However, to ensure the dynamic data management and a better understanding of HSPS operations, it is crucial to analyze and forecast these digital records for decision-making and smart control. Up to now, deep learning algorithms have only been applied sparsely in the field of renewable energy power forecasting. In this paper, we apply an auto-configurable middleware based on a Long Short Term Memory (LSTM) model for several forecasting time dimensions to choose the significant timescale for learning setting. The results show that our deep-learning model has good performances compared to the Support Vector Machine (SVM) model for the whole proposed learning timescale. However, the Auto-Regressive Integrated Moving Average (ARIMA) seems better than our proposed auto-LSTM algorithm, but it takes much execution time for 15 min and 30 min-ahead forecasting. Thus, the day-ahead forecasting is the most efficient timescale in our case.

[1]  Mohammad Teshnehlab,et al.  Robust deep neural network for wind speed prediction , 2015, 2015 4th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS).

[2]  Dazhi Yang,et al.  Satellite image analysis and a hybrid ESSS/ANN model to forecast solar irradiance in the tropics , 2014 .

[3]  Hongkun Chen,et al.  Wind power prediction and pattern feature based on deep learning method , 2014, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC).

[4]  Firooz B. Saghezchi,et al.  Towards a secure network architecture for smart grids in 5G era , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[5]  T. Hoff,et al.  Validation of short and medium term operational solar radiation forecasts in the US , 2010 .

[6]  James Pustejovsky,et al.  Fine-grained event learning of human-object interaction with LSTM-CRF , 2017, ESANN.

[7]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[8]  Robert Jenssen,et al.  An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting , 2017, ArXiv.

[9]  C. K. Simoglou,et al.  Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting , 2016, 2016 IEEE International Energy Conference (ENERGYCON).

[10]  Ian T. Nabney,et al.  Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models , 2010 .

[11]  Lovekesh Vig,et al.  TimeNet: Pre-trained deep recurrent neural network for time series classification , 2017, ESANN.

[12]  Borhan Molazem Sanandaji,et al.  Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting , 2017, ArXiv.

[13]  Mohamed E. El-Hawary,et al.  An overview of forecasting techniques for load, wind and solar powers , 2017, 2017 IEEE Electrical Power and Energy Conference (EPEC).

[14]  J. Marcos,et al.  Control Strategies to Smooth Short-Term Power Fluctuations in Large Photovoltaic Plants Using Battery Storage Systems , 2014 .

[15]  A. G. Bakirtzis,et al.  Application of time series and artificial neural network models in short-term forecasting of PV power generation , 2013, 2013 48th International Universities' Power Engineering Conference (UPEC).

[16]  Zechun Hu,et al.  Photovoltaic and solar power forecasting for smart grid energy management , 2015 .

[17]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[18]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[19]  Xiaojia Wang.  Electricity Consumption Forecasting in the Age of Big Data , 2013 .

[20]  Phone Lin,et al.  A survey on NB-IoT downlink scheduling: Issues and potential solutions , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[21]  Shun-Ren Yang,et al.  Modeling LTE group paging mechanism for Machine-Type Communications , 2015, 2015 International Wireless Communications and Mobile Computing Conference (IWCMC).

[22]  Bernhard Sick,et al.  Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[23]  Dumitru Erhan,et al.  Show and Tell: Lessons Learned from the 2015 MSCOCO Image Captioning Challenge , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.