2021· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
This study presents a comprehensive analysis of renewable energy forecasting using deep learning techniques, focusing on short-term and medium-term prediction horizons, and shows that deep learning models provide significantly better forecasting accuracy, particularly under highly variable weather conditions.
Abstract
The accurate prediction of renewable energy generation is essential for the efficient operation, planning, and optimization of modern power systems. The increasing integration of renewable sources such as solar photovoltaic (PV) and wind energy introduces uncertainty because their output depends on meteorological conditions. Traditional statistical and physics-based forecasting methods often fail to effectively capture the complex, nonlinear, and stochastic characteristics of renewable energy time-series data. Deep learning models have recently emerged as powerful tools for renewable energy forecasting due to their ability to automatically learn hierarchical representations from large-scale historical and exogenous data. This study presents a comprehensive analysis of renewable energy forecasting using deep learning techniques, focusing on short-term and medium-term prediction horizons. The paper reviews the evolution of forecasting approaches from conventional time-series models to advanced deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, gated recurrent units (GRUs), and hybrid deep learning models. The proposed forecasting framework integrates meteorological variables, historical power generation data, and temporal features to improve prediction accuracy and robustness. A unified deep learning–based forecasting scheme is presented, including data collection, preprocessing, feature engineering, model training, validation, and deployment. Mathematical formulations of the learning process and loss optimization are also provided to establish a theoretical foundation. Extensive experiments are conducted on real-world renewable energy datasets obtained from publicly available sources to demonstrate the superiority of deep learning models over traditional methods. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that deep learning models provide significantly better forecasting accuracy, particularly under highly variable weather conditions. The study highlights the importance of model selection, hyperparameter optimization, and data quality in renewable energy forecasting and serves as a useful reference for researchers and practitioners working on intelligent energy management and decision-support systems.
Deep learning–based surrogate models can generate sizing decisions comparable to those obtained using PSO with only a fraction of the computational effort, and provide a fast and practical alternative to conventional iterative optimization methods for component sizing in smart grid and sustainable energy planning applications.
A Meta-Optimized Deep Learning Fusion with Nonlinear Stacking (MODLF-NS) for forecasting RE usage, which combines Elman Recurrent Neural Networks (ERNN) with Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) to enhance model performance.
A. A. Bafti, M. Rezaei· Journal of Environmental Inf...· 0 citations
The paper gives a Hybrid Evolutionary Optimization and Neural Network Model of climate adaptive renewable energy forecasting. The need to increase renewable energy has led to the creation of more precise and flexible forecasting tools. This paper integrates evolutionary optimization techniques, including Genetic algorithms (GA) and Particle Swarm optimization (PSO), with deep learning neural networks, specifically Long short-term memory (LSTM), to predict renewable energy production using solar and wind energy. The methodology will consist of gathering up-to-date environmental measurements (temperature, wind speed, and solar radiation), as well as the previous data on energy generation by using renewable resources. The hyperparameters of the deep learning model are optimized using evo0.lutionary optimization algorithms to make accurate predictions of the model under different climatic conditions. The proposed hybrid model was compared to the conventional models, such as ARIMA and SVM, and the outcomes indicate that it has a better performance with respect to the accuracy of the prediction, the Mean Squared Error (MSE), the Root Mean Squared Error (RMSE), and the R2 value. The hybrid model also saves a lot of time when it comes to predicting failure, thus it is more effective in proactive energy management. Also, the model can be adjusted to changing conditions of the environment and offers real-time predictions and useful information concerning the optimization of energy production and its integration into the power grid. The results indicate that this mixed method has the potential to maximize the accuracy and effectiveness of renewable energy prediction, which will further result in improved energy grid management and low operation costs.
Nidhi Mishra, Aakansha Soy· 2026 International Conferenc...· 0 citations
An optimized forecasting framework using Long Short-Term Memory (LSTM) networks is introduced, confirming the model’s excellent accuracy and its value for improving power system dispatch and resource planning.
Solar energy has become an important source of renewable energy towards supporting the increased electricity demand in the world and minimizing reliance on fossil energy. Nevertheless, solar irradiance is intermittent and unreliable, which necessitates the precise prediction of solar energy to promote effective integration and energy management into the grid. This review bridges a gap in the research to unify the different machine learning (ML) and deep learning (DL) methods to predict solar power and solar resource, with the need to have a comparative evaluation of the performance, interpretability, and adaptability of the methods. This paper presents a systematic review of the recent developments in ML and DL based models applied in the analysis of solar power and solar resources forecasting, such as standalone, hybrid, and ensemble models. The research topic is to determine the appropriate parameters of input, feature selection techniques, and forecasting horizons that improve the accuracy and precision of the model. The methodology that was adopted is a comprehensive comparative analysis of the previous research with an emphasis on the strengths, weaknesses, and possibilities of the various predictive models. The findings prove that hybrid and ensemble models are invariably more successful compared to traditional models, in terms of accuracy and reliability. The review shows that the synergy of AI-based solutions can determine significant improvements in terms of accuracy of forecasting, energy scheduling, and grid stability. Taking all of this into consideration, the provided review fits into the smart prediction model evolution for effective and environmentally friendly use of solar energy. The key contributions of this paper and the main highlights are as follows: Introduces the general classification of ML and DL methods applied on PV power and solar resource prediction. Compares single, hybrid, and collaborative forecasting destructions at diverse timescales. Discusses how feature selection and meteorological parameters can be used to improve the accuracy of the model. Recognizes interpretability, data availability, as well as generalization challenges in AI-based forecasting. Recommends future research initiatives, such as transfer learning, probabilistic forecasting, and explainable AI usages. Proposed a graphical taxonomy to have a better conceptual knowledge and viable model choice. Provides an updated synthesis (2020–2025) of literature to ensure contemporary relevance to solar PV forecasting research. Introduces the general classification of ML and DL methods applied on PV power and solar resource prediction. Compares single, hybrid, and collaborative forecasting destructions at diverse timescales. Discusses how feature selection and meteorological parameters can be used to improve the accuracy of the model. Recognizes interpretability, data availability, as well as generalization challenges in AI-based forecasting. Recommends future research initiatives, such as transfer learning, probabilistic forecasting, and explainable AI usages. Proposed a graphical taxonomy to have a better conceptual knowledge and viable model choice. Provides an updated synthesis (2020–2025) of literature to ensure contemporary relevance to solar PV forecasting research.
Dheeraj Sharma, C. Kumar, Manish Kumar· Journal of Electrical System...· 0 citations
The integration of renewable energy, such as solar and wind power, is essential for the development of zero-carbon smart cities. Reliable short-term and medium-term predictions help reduce dependence on fossil-fuel reserves and facilitate more reasonable decisions in urban systems. This study develops a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for forecasting multi-source renewable energy. The convolutional layers can extract local temporal patterns from meteorological variables, while the LSTM layers model sequential dependencies over longer horizons. To improve transparency, Local Interpretable Model-agnostic Explanations (LIME) is applied to examine how input variables influence predictions. Experiments are conducted using data from the National Renewable Energy Laboratory, including the National Solar Radiation Database and the Wind Integration National Dataset. Compared with standalone CNNs, LSTMs, and conventional learning models, the hybrid model achieves lower prediction error, with Mean Absolute Percentage Error typically below 3% for forecasting next-day values. Results indicate that higher forecasting accuracy can contribute to reduced renewable curtailment and to more stable, reasonable decisions in smart cities. The proposed framework offers a practical, interpretable approach to renewable energy forecasting in urban systems.
Jia-Cheng Ran· Applied and Computational En...· 0 citations
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