AI Meets Sustainability: Drumil Joshi's Vision for a Greener Future

Today, we are honoured to interview Mr Drumil Joshi currently serving as Monitoring & Diagnostics Analyst (Renewables) at Southern Power USA for his acclaimed international research paper, "An Efficient Supervised Machine Learning Model Approach for Forecasting of Renewable Energy to Tackle Climate Change," published in the International Journal of Computer Science Engineering and Information Technology Research. The study has gained significant traction with 50+ citations and 1000+ reads on platforms like Google Scholar and ResearchGate.

Drumil's research uses real-world data from the European Network of Transmission System Operators for Electricity (ENTSOE), which consolidates live energy data from all participating European nations. This makes his study not only groundbreaking but also essential for policymakers and industries worldwide seeking data-driven solutions to optimize renewable energy operations.

By leveraging advanced machine learning techniques, AI enables highly accurate energy forecasts for solar and wind systems on a global scale, playing a pivotal role in reducing energy wastage, enhancing grid efficiency, and minimizing reliance on fossil fuels. Beyond forecasting, AI’s capability for predictive maintenance ensures the longevity and reliability of renewable energy installations, significantly reducing downtime and operational costs. It also streamlines energy distribution worldwide, overcoming challenges like resource inconsistency and ensuring energy is delivered where it’s needed most. By advancing renewable energy systems, this approach empowers the global fight against climate change, making clean energy more accessible, scalable, and dependable paving the way for a sustainable, greener future for all.

Q1: So Drumil What motivated you to focus on renewable energy forecasting using machine learning, and how does your research contribute to tackling climate change?

My motivation comes from a genuine desire to be part of the solution to climate change. Renewable energy isn’t just an option, it’s the future. But to make that future reliable and sustainable, we need accurate forecasting. If we can predict how much renewable energy will be available and match that with demand, we can make better use of what we have, reduce waste, and rely less on fossil fuels.

Machine learning plays a huge role in this. It allows us to make sense of complex patterns in energy generation and usage. My research focuses on creating forecasts that are not only accurate but also practical for industries and policymakers. It’s about making renewable energy as dependable as traditional sources, and in doing so, helping the world transition to a cleaner, greener future.

Q2: Can you explain the unique combination of machine learning algorithms you used, and why they were chosen for this study?

We approached this problem with a combination of models to tackle the unique challenges of renewable energy forecasting. Random Forest was a natural choice because it’s great at handling non-linear data and identifying the most important factors influencing energy output. That gives us insights into what really drives renewable energy trends.

On the other hand, we used time-series models like LSTM (Long Short-Term Memory) networks because they’re exceptional at recognizing patterns over time. Renewable energy is highly dependent on weather, seasons, and other temporal factors, so LSTMs help us capture those nuances. By combining these methods, we’ve created forecasts that are not just accurate but also adaptable to the unpredictable nature of renewables. It’s all about finding the right tools for the job and making sure the insights are actionable.

Q3: How did you ensure the accuracy and reliability of your model, achieving a SMAPE value of 1-2?

Achieving a SMAPE value of 1-2 wasn’t an accident, it took a lot of fine-tuning and a focus on the details. We started with robust preprocessing to clean and prepare the data, making sure that irrelevant or noisy features didn’t interfere. Feature extraction played a key role too, as we carefully identified the most meaningful factors that impact renewable energy output.

To ensure reliability, we used cross-validation to test the model across different data splits and applied hyperparameter optimization to get the best possible performance from each algorithm. On top of that, we incorporated ensemble methods, which helped reduce overfitting and balance out any biases. Frequent benchmarking against historical data and incorporating real-time feedback allowed us to continuously refine the model, ensuring that it stayed accurate and reliable. It’s a constant cycle of learning and improvement.

Q4: What challenges did you face while working with live data from ENTSOE’s Transparency Platform, and how did you overcome them?

Working with live data is always tricky, and ENTSOE’s Transparency Platform was no exception. One of the biggest challenges was dealing with missing or noisy data—gaps in reporting or anomalies can really throw off predictions. To handle this, we implemented data imputation techniques to fill in the gaps and ensure consistency.

Another issue was ingesting the data in real time while maintaining performance. For this, we developed a streamlined data pipeline that could process information efficiently without introducing delays. Lastly, every country has its own way of reporting data, which meant dealing with a lot of variability. We addressed this by creating a normalization framework that standardized the inputs, ensuring we could harmonize the data across all sources. These steps not only made the process more efficient but also ensured the consistency and accuracy of our forecasts."

Q5: Your research emphasizes the integration of renewable energy forecasting into real-world decision-making. How do you see this model being applied at a policy or industry level?

"This model has the potential to be a game-changer for both policymakers and industries. At the policy level, it can support setting realistic renewable energy targets by providing accurate insights into energy availability and demand patterns. It also helps governments prioritize investments in grid infrastructure and renewable projects, ensuring a smoother transition to clean energy.

In the industry, the applications are even broader. Accurate forecasts enable better operational planning, from optimizing energy distribution to improving storage strategies. It can also play a pivotal role in power trading by giving stakeholders confidence in the reliability of renewable sources. Essentially, this research bridges the gap between raw data and actionable decisions, empowering stakeholders to make renewable energy more affordable, reliable, and scalable."

Q6: The deployment of your model through a web app is impressive. What role do you envision this app playing for stakeholders in the renewable energy sector?

"The web app was designed with accessibility and practicality in mind. For utilities, grid operators, and policymakers, it serves as a powerful tool to visualize and interact with energy forecasts in real time. It provides actionable insights on energy distribution, storage needs, and grid stability, which are critical for daily operations and long-term planning.

What makes the app truly impactful is its ability to simplify complex analytics. By turning dense data into intuitive charts and forecasts, it fosters transparency and collaboration among key players in the renewable energy ecosystem. Ultimately, it’s about making advanced forecasting technology accessible to those who can drive real-world change."

Q7: Looking ahead, how do you plan to expand or refine this model to address renewable energy forecasting in other regions or for different energy sources?

"The next step is to test and adapt the model for regions with varying climates, energy networks, and renewable capacities. Each geographic zone has unique challenges, and incorporating localized datasets will be crucial for ensuring accuracy. I’m particularly interested in extending the model to emerging energy sources like geothermal and tidal energy, as they hold significant potential for the future.

Additionally, I plan to explore advanced AI techniques like federated learning. This would allow the model to learn from distributed datasets while maintaining data privacy, making it scalable across global energy networks. The ultimate goal is to create a universal, adaptable forecasting tool that can drive the adoption of renewables anywhere in the world."

About Drumil Joshi

Drumil Joshi is a globally recognized leader in Artificial Intelligence (AI) and Machine Learning (ML), known for pioneering the application of these technologies to tackle critical environmental challenges. As a Monitoring & Diagnostics Analyst at Southern Power, Drumil optimizes renewable energy systems, ensuring maximum efficiency and reliability through AI-driven forecasting and predictive maintenance. His innovative work significantly reduces operational costs, enhances grid stability, and accelerates the shift to clean energy solutions—aligning technology with sustainability goals. Notably, his efforts have earned him a spotlight on NDTV International 24x7, reaching a global audience of 1.3 billion people, and recognition in CEO Weekly Magazine for driving meaningful change in the renewable energy sector.

Beyond his corporate achievements, Drumil’s influence extends to academic and research communities. He has published 10 international research papers with 100+ citations, holds an Indian Patent on Machine Learning for Corporate Ethics, and has contributed as an Editorial Manager for prestigious journals under the American Psychological Association. He further serves as an Editorial Board Member for many renowned journals like the International Journal of Electronic Trade, International Journal of Diplomacy and Economy and as a Technical Peer Reviewer for leading platforms like IEEE Access & IEEE Transactions. Drumil’s expertise, thought leadership, and vision for a sustainable future position him at the forefront of AI innovation, making him a driving force in the global effort to combat climate change and revolutionize renewable energy systems.