Journal
ENERGY REPORTS
Volume 8, Issue -, Pages 10230-10243Publisher
ELSEVIER
DOI: 10.1016/j.egyr.2022.08.009
Keywords
Echo state network; Renewable energy; Power generation forecasting; CNN; Hybrid model; ESNCNN
Categories
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIT) [2019M3F2A1073179]
Ask authors/readers for more resources
The article introduces a lightweight ESNCNN model developed to accurately predict renewable energy production, addressing the challenge of integrating renewable energy into smart grids due to its randomness, intermittency, and volatility. The ESNCNN model combines ESN for learning nonlinear mapping relationships and CNN for extracting spatial information from renewable energy data, achieving state-of-the-art performance in experiments.
Renewable energy (RE) offers major environmental and economic benefits compared to nuclear and fuel-based energy; however, the data used for RE include significant randomness, intermittent behaviour, and strong-volatility, hindering their integration into smart grids. Accurate RE prediction is a promising solution to this problem and can provide effective planning and management services. Various predictive models have been developed to improve the prediction performance for better energy management. However, current works focus on improving the prediction accuracy, which is a requirement of power systems, without considering the time complexity of their methodologies. Considering these limitations, we develop a lightweight ESNCNN model for accurate RE prediction, in which an ESN learns the nonlinear mapping relationship and a CNN extracts the spatial information from RE data. The ESN and CNN layers are linearly connected via residual connections to avoid the vanishing gradient problem. Finally, we incorporate fully connected layers to enhance and select the optimal ESNCNN features to predict the future energy production. Our ESNCNN model is evaluated based on RE benchmarks, using various evaluation metrics such as MSE, MBE, MAE and RMSE, and achieves state-of-the-art performance. Further experiments are performed with different machine learning, deep learning, and hybrid models to select the optimum model. To fully assess the generalisation ability of the proposed ESNCNN, additional experiments are performed over electricity consumption datasets, which reveal an extensive decrease in error rates compared to other state-of -the-art approaches. Our model therefore represents a new paradigm for finding an energy equilibrium between grid and energy production resources using a single ESNCNN platform. Our results indicate a substantial reduction in the error rates over the RE dataset (MSE (5.01%), MAE (5.49%), and RMSE (3.76%)) and the electricity consumption dataset (MSE (5.37%), MAE (7.63%), RMSE (0.047%), and MBE (1.2%)). (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available