Next Article in Journal
Role of Personalization in Continuous Use Intention of Mobile News Apps in India: Extending the UTAUT2 Model
Previous Article in Journal
Semi-Automatic Corpus Expansion and Extraction of Uyghur-Named Entities and Relations Based on a Hybrid Method
Previous Article in Special Issue
Weakly Supervised Learning for Evaluating Road Surface Condition from Wheelchair Driving Data
Open AccessArticle

Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology

Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China
School of Computer Engineering, Weifang University, Weifang 261061, China
Zhejiang Huayun Information Technology Co., Ltd., Hangzhou 310008, China
Nanhu College, Jiaxing University, Jiaxing 314001, China
Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China
Author to whom correspondence should be addressed.
Information 2020, 11(1), 32;
Received: 19 December 2019 / Revised: 1 January 2020 / Accepted: 2 January 2020 / Published: 6 January 2020
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU). View Full-Text
Keywords: short-term forecasting; solar irradiance; gated recurrent unit; attention mechanism short-term forecasting; solar irradiance; gated recurrent unit; attention mechanism
Show Figures

Figure 1

MDPI and ACS Style

Yan, K.; Shen, H.; Wang, L.; Zhou, H.; Xu, M.; Mo, Y. Short-Term Solar Irradiance Forecasting Based on a Hybrid Deep Learning Methodology. Information 2020, 11, 32.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop