Predictive Analysis of Photovoltaic Power Generation Using Deep Learning

A novel deep learning approach is proposed for the predictive analysis of trends in energy related time series, in particular those relevant to photovoltaic systems. Aim of the proposed approach is to grasp the trend of the time series, namely, if the series goes up, down or keep stable, instead of predicting the future numerical value. The modeling system is based on Long Short-Term Memory networks, which are a type of recurrent neural network able to extract information in samples located very far from the current one. This new approach has been tested in a real-world case study showing good robustness and accuracy.

[1]  Kok Soon Tey,et al.  Forecasting of photovoltaic power generation and model optimization: A review , 2018 .

[2]  George G. Szpiro.  Forecasting chaotic time series with genetic algorithms , 1997 .

[3]  Zhe Zhang,et al.  A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation , 2016, IEEE Journal of Biomedical and Health Informatics.

[4]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[5]  A. Shabri,et al.  A comparison of time series forecasting using support vector machine and artificial neural network model , 2010 .

[6]  Rafał Weron,et al.  Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks , 2018, 1805.06649.

[7]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..

[8]  Massimo Panella,et al.  Embedding of time series for the prediction in photovoltaic power plants , 2016, 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC).

[9]  Massimo Panella,et al.  Selection of clinical features for pattern recognition applied to gait analysis , 2017, Medical & Biological Engineering & Computing.

[10]  Soteris A. Kalogirou,et al.  Machine learning methods for solar radiation forecasting: A review , 2017 .

[11]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[12]  Juan José González de la Rosa,et al.  Weather forecasts for microgrid energy management: Review, discussion and recommendations , 2018, Applied Energy.

[13]  Christopher Heard,et al.  Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model , 2016 .

[14]  Mariesa L. Crow,et al.  Multi-Objective Dynamic Economic Dispatch with Demand Side Management of Residential Loads and Electric Vehicles , 2017 .

[15]  Johan A. K. Suykens,et al.  Financial time series prediction using least squares support vector machines within the evidence framework , 2001, IEEE Trans. Neural Networks.

[16]  Sahil Shah,et al.  Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques , 2015, Expert Syst. Appl..

[17]  Antonello Rizzi,et al.  A recursive algorithm for fuzzy min-max networks , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[18]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[19]  Jaime Lloret,et al.  Artificial neural networks for short-term load forecasting in microgrids environment , 2014 .

[20]  Massimo Panella,et al.  A Neural Network Based Prediction System of Distributed Generation for the Management of Microgrids , 2019, IEEE Transactions on Industry Applications.

[21]  Massimo Panella,et al.  Shapes classification of dust deposition using fuzzy kernel-based approaches , 2016 .

[22]  Antonello Rizzi,et al.  Refining accuracy of environmental data prediction by MoG neural networks , 2003, Neurocomputing.