Solar Energy Prediction using Meteorological Variables

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

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Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models

Concurrency and Computation Practice and Experience ◽

10.1002/cpe.3631 ◽

2015 ◽

Vol 28 (4) ◽

pp. 1261-1274 ◽

Cited By ~ 7

Author(s):

R. Martin ◽

R. Aler ◽

J. M. Valls ◽

I. M. Galvan

Keyword(s):

Machine Learning ◽

Solar Energy ◽

Machine Learning Techniques ◽

Energy Prediction ◽

Numerical Weather ◽

Numerical Weather Models ◽

Learning Techniques ◽

Weather Models

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Short Time Solar Power Forecasting Using Persistence Extreme Learning Machine Approach

E3S Web of Conferences ◽

10.1051/e3sconf/202129401002 ◽

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.

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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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A New Approach for Meteorological Variables Prediction at Kuala Lumpur, Malaysia, Using Artificial Neural Networks: Application for Sizing and Maintaining Photovoltaic Systems

Journal of Solar Energy Engineering ◽

10.1115/1.4005754 ◽

2012 ◽

Vol 134 (2) ◽

Cited By ~ 6

Author(s):

Tamer Khatib ◽

Azah Mohamed ◽

M. Mahmoud ◽

K. Sopian

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wind Speed ◽

Relative Humidity ◽

Solar Energy ◽

Ambient Temperature ◽

Meteorological Variables ◽

Mean Bias Error ◽

Artificial Neural ◽

Prediction Approach

This research presents a new meteorological variables prediction approach for Malaysia using artificial neural networks. The developed model predicts four meteorological variables using sun shine ratio, day number, and location coordinates. These meteorological variables are solar energy, ambient temperature, wind speed, and relative humidity. However, three statistical values are used to evaluate the proposed model. These statistical values are mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). Based on results, the developed model predicts accurately the four meteorological variables. The MAPE, RMSE, and MBE in predicting solar radiation are 1.3%, 5.8 (1.8%), and 0.9 (0.3%), respectively, while the MAPE, RMSE, and MBE values for ambient temperature prediction are 1.3%, 0.4 (1.7%), and 0.1 (0.4%), respectively. In addition, the MAPE, RMSE, and MBE values in relative humidity prediction are 3.2%, 3.2, and 0.2. As for wind speed prediction, it is the worst in accuracy among the predicted variables because the MAPE, RMSE, and MBE values are 28.9%, 0.5 (31.3%), and 0.02 (1.25%). Such a developed model helps in sizing photovoltaic (PV) systems using solar energy and ambient temperature records. Moreover, wind speed and relative humidity records could be used in estimating dust concentration group which leads to dust deposition on a PV system.

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

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-14-00006.1 ◽

2015 ◽

Vol 96 (8) ◽

pp. 1388-1395 ◽

Cited By ~ 20

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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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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