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

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.

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Deep Learning based Models for Solar Energy Prediction

Advances in Science Technology and Engineering Systems Journal ◽

10.25046/aj060140 ◽

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

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

Proceedings of the Seventh International Conference on the Internet of Things - IoT '17 ◽

10.1145/3131542.3131544 ◽

2017 ◽

Cited By ~ 14

Author(s):

Frank Alexander Kraemer ◽

Doreid Ammar ◽

Anders Eivind Braten ◽

Nattachart Tamkittikhun ◽

David Palma

Keyword(s):

Solar Energy ◽

Energy Prediction ◽

Weather Forecasts

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

Energy ◽

10.1016/j.energy.2015.10.054 ◽

2015 ◽

Vol 93 ◽

pp. 1918-1930 ◽

Cited By ~ 27

Author(s):

P.G. Kosmopoulos ◽

S. Kazadzis ◽

K. Lagouvardos ◽

V. Kotroni ◽

A. Bais

Keyword(s):

Solar Energy ◽

Operational Model ◽

Energy Prediction

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

Machine Learning and Knowledge Extraction ◽

10.3390/make3040047 ◽

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.

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