Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm

The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast...

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Bibliographic Details
Published inEnergies (Basel) Vol. 13; no. 8; p. 1879
Main Authors Aprillia, Happy, Yang, Hong-Tzer, Huang, Chao-Ming
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2020
Subjects
Accuracy
Algorithms
Clean energy
Climate change
Climate models
convolutional neural network
day ahead forecasting
Deep learning
Economic forecasting
Forecasting
Humidity
Long short-term memory
Meteorological data
Methods
Neural networks
Outdoor air quality
Photovoltaic cells
Photovoltaics
Power
PV power forecasting
Regression analysis
renewable energy
salp swarm algorithm
Short term
Support vector machines
Variables
Weather forecasting
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Summary:The high utilization of renewable energy to manage climate change and provide green energy requires short-term photovoltaic (PV) power forecasting. In this paper, a novel forecasting strategy that combines a convolutional neural network (CNN) and a salp swarm algorithm (SSA) is proposed to forecast PV power output. First, the historical PV power data and associated weather information are classified into five weather types, such as rainy, heavy cloudy, cloudy, light cloudy and sunny. The CNN classification is then used to determine the prediction for the next day’s weather type. Five models of CNN regression are established to accommodate the prediction for different weather types. Each CNN regression is optimized using a salp swarm algorithm (SSA) to tune the best parameter. To evaluate the performance of the proposed method, comparisons were made to the SSA based support vector machine (SVM-SSA) and long short-term memory neural network (LSTM-SSA) methods. The proposed method was tested on a PV power generation system with a 500 kWp capacity located in south Taiwan. The results showed that the proposed CNN-SSA could accommodate the actual generation pattern better than the SVM-SSA and LSTM-SSA methods.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13081879