Deep Learning based Models for Solar Energy Prediction

2021 ◽  
Vol 6 (1) ◽  
pp. 349-355
Author(s):  
Imane Jebli ◽  
Fatima-Zahra Belouadha ◽  
Mohammed Issam Kabbaj ◽  
Amine Tilioua
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Energy Prediction

scholarly journals A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

2021 ◽  
Vol 3 (4) ◽  
pp. 946-965
Author(s):  
Sourav Malakar ◽  
Saptarsi Goswami ◽  
Bhaswati Ganguli ◽  
Amlan Chakrabarti ◽  
Sugata Sen Roy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Weather Conditions ◽  
Feature Representation ◽  
Energy Prediction ◽  
Pv Systems ◽  
Proposed Model ◽  
Energy Domain ◽  
Learning Architectures ◽  
Gated Recurrent Unit

Complex weather conditions—in particular clouds—leads to uncertainty in photovoltaic (PV) systems, which makes solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. There are quite a few choices of deep-learning architectures, among which Bidirectional Gated Recurrent Unit (BGRU) has apparently not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India.

Energy Supply Management Using the Solar Energy Prediction in Smart Grid

2016 ◽  
Vol 11 (5) ◽  
pp. 486
Author(s):  
Abdelilah Kahaji ◽  
Rachid Alaoui ◽  
Sadik Farhat ◽  
Lahoussine Bouhouch
Keyword(s):  
Solar Energy ◽  
Smart Grid ◽  
Energy Supply ◽  
Supply Management ◽  
Energy Prediction

scholarly journals Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

2021 ◽  
Vol 294 ◽  
pp. 01002
Author(s):  
Xiaoyan Xiang ◽  
Yao Sun ◽  
Xiaofei Deng
Keyword(s):  
Solar Energy ◽  
Extreme Learning Machine ◽  
Power Output ◽  
Smart Grids ◽  
Large Scale ◽  
Solar Power ◽  
Learning Algorithm ◽  
Power Performance ◽  
Energy Prediction ◽  
Learning Machine

Solar energy in nature is irregular, so photovoltaic (PV) power performance is intermittent, and highly dependent on solar radiation, temperature and other meteorological parameters. Accurately predicting solar power to ensure the economic operation of micro-grids (MG) and smart grids is an important challenge to improve the large-scale application of PV to traditional power systems. In this paper, a hybrid machine learning algorithm is proposed to predict solar power accurately, and Persistence Extreme Learning Machine(P-ELM) algorithm is used to train the system. The input parameters are the temperature, sunshine and solar power output at the time of i, and the output parameters are the temperature, sunshine and solar power output at the time i+1. The proposed method can realize the prediction of solar power output 20 minutes in advance. Mean absolute error (MAE) and root-mean-square error (RMSE) are used to characterize the performance of P-ELM algorithm, and compared with ELM algorithm. The results show that the accuracy of P-ELM algorithm is better in short-term prediction, and P-ELM algorithm is very suitable for real-time solar energy prediction accuracy and reliability.

Deep learning-based feature engineering methods for improved building energy prediction

2019 ◽  
Vol 240 ◽  
pp. 35-45 ◽  
Author(s):  
Cheng Fan ◽  
Yongjun Sun ◽  
Yang Zhao ◽  
Mengjie Song ◽  
Jiayuan Wang
Keyword(s):  
Deep Learning ◽  
Building Energy ◽  
Feature Engineering ◽  
Energy Prediction

Assessment of Photovoltaic Capabilities in Urban Environments: A Case Study of Islamabad, Pakistan

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Muhammad Zubair ◽  
Sajid Ghuffar ◽  
Muhammad Shoaib ◽  
Ahmed Bilal Awan ◽  
Abdul Rauf Bhatti
Keyword(s):  
Deep Learning ◽  
Solar Energy ◽  
Urban Areas ◽  
Capital City ◽  
Coefficient Of Performance ◽  
Energy Yield ◽  
Cooling Load ◽  
Zero Energy ◽  
Net Zero Energy ◽  
Net Zero

Abstract Photovoltaic (PV) estimation in an urban environment requires detection of rooftop area, design of PV system based on optimization on PV placement distance and the study of additional benefit of lower cooling load of building by shading provided by PV panels. The study is aimed at policymakers to introduce renewable energy policy toward net-zero energy buildings in urban areas. In this research, the capital city of Pakistan, Islamabad, is analyzed for rooftop PV capabilities using deep learning algorithms. The area of the rooftop is calculated by extracting buildings in high-resolution satellite imagery using a deep learning algorithm. The site location is analyzed for available solar energy resources. The distance between the rooftop-PV array is optimized based on self-shading losses, coefficient of performance, energy yield, net-zero energy analysis, and reduction of cooling load of the building provided by PV arrays as shading devices. The 40-km2 area of Islamabad considered in this research can generate 1038 GWh of solar energy annually from its 4.3-km2 rooftop area by installed capacity of 447 MW PV panels rows placed at 0.75 m apart. The electricity generated by Islamabad can curtail residential load from the national grid and form a near net-zero energy zone while the electrical energy from the grid can be provided to the industries to enhance the economy and reduce unemployment in Pakistan.

Solar energy prediction for constrained IoT nodes based on public weather forecasts

2017 ◽  
Author(s):  
Frank Alexander Kraemer ◽  
Doreid Ammar ◽  
Anders Eivind Braten ◽  
Nattachart Tamkittikhun ◽  
David Palma
Keyword(s):  
Solar Energy ◽  
Energy Prediction ◽  
Weather Forecasts

Solar energy prediction and verification using operational model forecasts and ground-based solar measurements

Energy ◽  
2015 ◽  
Vol 93 ◽  
pp. 1918-1930 ◽  
Author(s):  
P.G. Kosmopoulos ◽  
S. Kazadzis ◽  
K. Lagouvardos ◽  
V. Kotroni ◽  
A. Bais
Keyword(s):  
Solar Energy ◽  
Operational Model ◽  
Energy Prediction

scholarly journals Solar Energy Prediction: An International Contest to Initiate Interdisciplinary Research on Compelling Meteorological Problems

2015 ◽  
Vol 96 (8) ◽  
pp. 1388-1395 ◽  
Author(s):  
Amy McGovern ◽  
David John Gagne ◽  
Jeffrey Basara ◽  
Thomas M. Hamill ◽  
David Margolin
Keyword(s):  
Solar Energy ◽  
Interdisciplinary Research ◽  
Energy Prediction ◽  
Meteorological Problems
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