Deep Learning Enhanced Solar Energy Forecasting with AI-Driven IoT

Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it...

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Bibliographic Details
Published in:	Wireless communications and mobile computing 2021, Vol.2021, p.1-11
Main Authors:	Zhou, Hangxia, Liu, Qian, Yan, Ke, Du, Yang
Format:	Article
Language:	eng
Subjects:	Algorithms Artificial intelligence Artificial neural networks Clustering Correlation analysis Datasets Deep learning Forecasting Forecasting techniques Genetic algorithms Internet of Things Machine learning Mathematical models Neural networks Outdoor air quality Photovoltaic cells Principal components analysis Smart grid Solar energy Support vector machines Time series Training Wireless sensor networks
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Summary:	Short-term photovoltaic (PV) energy generation forecasting models are important, stabilizing the power integration between the PV and the smart grid for artificial intelligence- (AI-) driven internet of things (IoT) modeling of smart cities. With the recent development of AI and IoT technologies, it is possible for deep learning techniques to achieve more accurate energy generation forecasting results for the PV systems. Difficulties exist for the traditional PV energy generation forecasting method considering external feature variables, such as the seasonality. In this study, we propose a hybrid deep learning method that combines the clustering techniques, convolutional neural network (CNN), long short-term memory (LSTM), and attention mechanism with the wireless sensor network to overcome the existing difficulties of the PV energy generation forecasting problem. The overall proposed method is divided into three stages, namely, clustering, training, and forecasting. In the clustering stage, correlation analysis and self-organizing mapping are employed to select the highest relevant factors in historical data. In the training stage, a convolutional neural network, long short-term memory neural network, and attention mechanism are combined to construct a hybrid deep learning model to perform the forecasting task. In the testing stage, the most appropriate training model is selected based on the month of the testing data. The experimental results showed significantly higher prediction accuracy rates for all time intervals compared to existing methods, including traditional artificial neural networks, long short-term memory neural networks, and an algorithm combining long short-term memory neural network and attention mechanism.
ISSN:	1530-8669 1530-8677