AI in Renewable Energy: Optimizing Solar, Wind & Grid Stability
Artificial intelligence in renewable energy is transforming solar and wind power with advanced forecasting, smarter grids.
In this article, we will explore how an energy company partnered with Omdena to implement AI-powered solutions to optimize renewable energy production, addressing challenges like unpredictable energy output from solar and wind sources through advanced machine learning models.
Challenge: Integrating Renewable Energy into the Grid
A major energy corporate had been encountering a series of big challenges in trying to connect renewables — for example, solar and wind power — with their existing grid. These obstacles were a result of the inherently volatile production process of renewable energy. In contrast to conventional fossil-fuel plants, renewable sources like solar and wind rely on weather factors (sunshine, wind speed) that can vary drastically, resulting in unreliable energy generation.
This variability posed several challenges:
- Lack of predictability in renewable energy generation: The company had difficulty predicting how much power to expect from its renewable sources, which made it almost impossible to plan for a stable grid as well as demand that was constantly changing.
- Creating quality dispatch plans: Without reliable forecasts, the company wasn’t able to efficiently plan how much power needed to be dispatched from each source (dispatched generation) in order to satisfy demand and keep the grid stable. This, in turn, resulted in wastage of resources and was threatening energy deficiency.
In order to solve these issues and release the potential of renewable energy, the company realized there had to be an AI served solution. AI provides a promising way to solve these problems by predicting renewables generation as well as demand with precision, which help the company to optimally integrate more renewables and participate in fighting global warming.
AI-Powered Solution for Solar and Wind Forecasting
The energy company collaborated with Omdena to create an AI solution for maximizing renewable energy generation. Omdena Data Scientists & Machine Learning Engineers worked on a range of machine learning models to predict renewable energy generation and demand. The machine learning algorithms were also used to forecast and alleviate possible grid bottlenecks. Specific algorithms utilized included:
- Time Series Forecasting Models: Algorithms like ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short-Term Memory) networks were used for their inherent capability to learn from the sequential data and predict future trends based on historical patterns. These models are especially capable of forecasting renewable energy output, which surface is often strongly temporal dependent.
- Regression Models: Methods Random Forest and Gradient Boosting were selected due to their ability to model non-linear relationships between features and feature interactions. These regression models can be used to forecast energy demand from the various inputs (weather, time of day, historical) etc.
- CNNs: Particularly applied to spatial data analysis, CNNs can analyze satellite images for resources on renewable energy and predict generation capacity based on the environmental conditions.
- Clustering Techniques: K-means clustering was employed to classify different consumer types or the grid components separately, in terms of detecting patterns and deviations in energy system.
The data sources they used to apply these models comprised an assortment of inputs to achieve accurate and robust predictions:
- Historical Energy Usage: Gave visibility into how energy was being consumed and when demand peaks occurred.
- Weather Information: Very important to forecast the renewable energy generation, extracted from met stations and satellite images such as solar radiation, wind velocity, rainfall and temperature.
- Geospatial Data: It is exploited to study the physical position of gridding utilities, possible locations for renewables and environmental limitations.
- Grid Status in Real Time: It was able to monitor and observe the real situation of the grid, such as energy (flow), transformers statuses, possible bottlenecks.
| Data Source | Description | Use Case |
|---|---|---|
| Historical Energy Usage | Records of past consumption patterns and peak demand. | Improves demand forecasting and resource allocation. |
| Weather Information | Data from meteorological stations & satellite imagery: solar irradiance, wind speed, rainfall, temperature. | Forecasts renewable generation based on climate variability. |
| Geospatial Data | Locations of grid infrastructure, renewable sites, and environmental constraints. | Identifies optimal renewable sites and supports grid planning. |
| Real-Time Grid Status | Current energy flow, transformer states, and bottlenecks. | Enables predictive maintenance and ensures grid stability. |
The data sources were compiled as follows:
- Data Cleaning & Preprocessing: Handling missing values, anomalies and inconsistencies in dataset to provide robust inputs for model training.
- Feature Engineering – Identify what features would be useful and how they could impact prediction results.
- Normalization/Standardization of Data: Rescaling the data such that the machine learning algorithms can interpret the scaled data for best results.
- Image from Unsplash by Kat (Time Series Decomposition:Factoring trends, seasonality and noise) Separating out Trends, Seasonality and Noise from your data for improved forecasts.
- Data Augmentation: For strengthening the datasets, synthetic data points are created in parts of the dataset where sample size is too low for training the model.
By integrating these machine learning models with extensive data integration strategies, the energy company was able to more accurately predict renewable energy activity and optimize grid operations.
Results: Higher Generation, Lower Emissions, Better Reliability
The AI-powered solution has been very successful. The solution has helped the energy company to increase renewable energy generation by 10% and reduce carbon emissions by 5%. The solution has also improved grid reliability by reducing the number of energy shortages and blackouts.
Key Benefits of AI in Renewable Energy
As a result of adopting the AI-driven solution, there have been various advantages for the energy company:
- Higher Renewable Energy Production: AI tools have enabled the firm to produce additional energy from renewable sources. This is realized by the optimal scheduling of resources, as well as accurate energy generation and consumption.
- Lower Carbon Emissions: With the AI solution managing energy production and distribution, carbon footprint of the company has been greatly reduced. This leads to a more sustainable and green operation.
- 1 Enhanced Grid Reliability: AI programs process massive volumes of data instantly, thereby predicting and preventing possible energy grid failures. This leads to higher stability and reliability of electricity supply for households.
- Cost Reductions: The energy company has seen a drop in operational costs due to the efficiency-driving capabilities of AI. Savings that can result from reduced energy wastage, less frequent repairs and optimized resource utilization that all adds up.
Conclusion
The development and implementation of the AI-powered solution to optimize renewable energy production has been a success for the energy company. The solution has helped the company to achieve its renewable energy goals and improve its bottom line.
Lessons Learned from AI Deployment
So, there are a couple things we can learn from this chronicle:
- As can be seen AI-based solutions have the potential to substantially improve renewable energy production.
- Accurate and effective machine learning models requires the collection and processing of a vast and diverse amount of data for the generation from renewable sources, weather observations at ground level, as well as grid information.
- It is also crucial to test the machine learning models on a held-out test set before deploying them into production.
- By doing so, energy companies can effectively design and deploy AI-driven solutions that enable them to bring more solar, wind and other sources of renewable energy online while cutting carbon emissions and improving the reliability of grids.
Omdena’s Role in Driving Innovation
Omdena played a key role in the development and implementation of the AI-powered solution. Omdena’s team of data scientists and machine learning engineers provided the following services:
- Data collection and preparation
- Model development and training
- Model evaluation
- Model deployment
Omdena also provided the energy company with access to its AI platform, which made it possible to develop and deploy the AI-powered solution quickly and efficiently.
Overall, Omdena’s involvement in the project was essential to its success. Omdena’s expertise in AI and machine learning, as well as its AI platform, were critical to the development and implementation of the AI-powered solution.
