Green Energy Forecasting
Explainable AI for Intelligent Green Energy Forecasting: Deep Learning with iHow Optimization Algorithm (iHOW)
Published online: 21 November 2025 by Mahmoud Shabrawy, Khaled Sh. Gaber, Marwa M. Eid, Amel Ali Alhussan, Doaa Sami Khafaga & El-Sayed M. El-kenawy
Transformative Precision in Renewable Energy Forecasting
This research introduces a novel framework that significantly enhances green energy forecasting accuracy and reliability. By integrating advanced deep learning with the iHOW optimization algorithm, we achieve unprecedented performance, crucial for effective energy management and sustainable power grids.
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Deep Analysis & Enterprise Applications
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A Novel Framework for Green Energy Demand Forecasting
Our study introduces a custom meta-heuristic algorithm, the iHow Optimization Algorithm (iHOW), integrated with advanced deep learning architectures, specifically Dynamic Temporal Convolutional Networks (DTCNs). This innovative combination delivers highly accurate predictions of green energy output, addressing the complex, non-linear, and multi-scale dependencies inherent in renewable energy data.
Enhanced Model Understandability and Accuracy
We present a novel combination method that utilizes sophisticated feature selection techniques alongside hyperparameter optimization. This process eliminates irrelevant information, simplifies the model, and significantly improves accuracy and interpretability. The result is a more straightforward and robust model that clarifies the relationship between input variables and green energy generation.
Superior Performance Across Diverse Metrics
Through numerous experimental trials, our iHOW-optimized DTCN method consistently outperformed other high-level deep learning algorithms across various forecasting measurements. This rigorous evaluation demonstrated that our approach achieves superior results without the need for hand-picked parameters, ensuring consistent and reliable performance.
Scalability for High-Dimensional Time-Series Data
Renewable energy systems often generate high-dimensional, noisy data. Our framework addresses these significant challenges by incorporating sophisticated feature selection approaches. This capability ensures flexible and scalable energy forecasting, even when confronted with complex and voluminous datasets, making it suitable for real-world enterprise applications.
Validated with Real-World Green Energy Datasets
To verify the efficacy and practical usefulness of our suggested framework, we utilized large, real-world green energy datasets. These datasets, spanning a ten-year period from 2008 to 2018, provided hourly energy usage records, including regional identifiers and energy types, ensuring the model's robust performance in various energy scenarios.
Foundation for Intelligent Energy Forecasting Systems
Our research establishes a strong basis for future advancements, paving the path for the development of intelligent energy forecasting systems. By combining real-time data streams, multi-source data fusion, and hybrid ensemble learning, we aim to further enhance the adaptability and predictive power of green energy forecasting solutions.
Key Performance Insight: Error Reduction
99.99% Reduction in Mean Squared Error (MSE) from initial baseline to iHOW-optimized model.Enterprise Process Flow
Data Preprocessing
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Data Splitting
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Baseline Model Training
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Feature Selection
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iHOW Optimization
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Performance Evaluation
| Model | MSE | RMSE | R² | NSE |
|---|---|---|---|---|
| iHOW + DTCN | 1.14E-05 | 5.33E-05 | 0.98036 | 0.98950 |
| HHO + DTCN | 0.0001664 | 0.0006456 | 0.96110 | 0.95649 |
| GWO + DTCN | 0.0001991 | 0.0007582 | 0.95521 | 0.95281 |
| PSO + DTCN | 0.0002318 | 0.0008708 | 0.95359 | 0.95085 |
Case Study: Real-World Green Energy Demand Forecasting
Our framework was rigorously tested using a comprehensive Green Energy Demand dataset, spanning a ten-year period from 2008 to 2018. This dataset provided hourly energy usage records, including regional identifiers and diverse energy types, such as wind, solar, and hydro.
The high-resolution temporal data enabled detailed observation of demand fluctuations across different seasons and activity types. This comprehensive coverage was crucial for identifying regular patterns in hourly and daily usage and capturing long-term developments in renewable energy adoption. The model's ability to learn and replicate these underlying patterns, validated on unseen data from 2019 to 2021, demonstrates its practical utility and robustness for real-world energy management scenarios.
Calculate Your Potential AI-Driven Savings
Understand the tangible impact of optimized green energy forecasting on your operational efficiency and cost savings. Adjust the parameters below to see your estimated ROI.
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Strategic Implementation Roadmap
Our iHOW-optimized DTCN framework outlines a clear, cognitive-inspired path to integrating advanced green energy forecasting within your enterprise, ensuring robust and adaptive deployment.
01. Data Collection & Initialization
Solutions collect raw environmental data, simulating human sensory perception, and initial positions are randomized across the search space to represent diverse experiential input.
02. Adaptive Learning & Information Exchange
Solutions modify their behavior by evaluating previous memory and population information, adjusting parameters via adaptive learning rates. This models interactive learning and incorporation of shared experiences.
03. Strategic Information Processing
Insights are extracted from the learned data to generate informed movement strategies. Solutions synthesize short-term experiences into meaningful navigation patterns, transforming raw data into action plans.
04. Knowledge Acquisition & Refinement
Knowledge is updated by integrating processed data with prior information. This stage ensures the retention of beneficial patterns and guides strategic repositioning in the solution space, forming long-term memory.
05. Expertise-Driven Optimization & Convergence
Final-stage solutions rely on cumulative expertise and refine their decisions through enhanced search rules, converging toward optimal results using collective memory and adaptive recombination.
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