Machine learning boosts solar forecasts in India: Study
As the world shifts towards renewable energy sources, solar power has become an increasingly important part of the mix. In India, where the sun shines bright for most of the year, harnessing solar energy can be a game-changer. However, the country’s diverse climate zones pose a significant challenge to predicting the sun’s strength, which is crucial for planning stable and cost-efficient solar power systems. A recent study by researchers at VIT (Vellore Institute of Technology) has found that machine learning can help improve solar forecasts in India, paving the way for more efficient and reliable solar power generation.
The study, which focused on predicting the Global Horizontal Irradiance (GHI), a measure of the sun’s strength, found that the Gaussian Process Regression (GPR) AI model was the most reliable tool for forecasting GHI across India’s extreme climates. GHI is a critical parameter in designing and operating solar power systems, including Stand-Alone Photovoltaic (SAPV) systems, which are commonly used in rural and remote areas.
The researchers used a dataset of GHI values from 16 stations across India, covering a range of climate zones, from the tropical south to the temperate north. They compared the performance of several machine learning models, including GPR, Artificial Neural Networks (ANN), and Support Vector Regression (SVR), in predicting GHI values. The results showed that GPR outperformed the other models, achieving an error reduction of up to 189.1% compared to other models.
This significant improvement in accuracy is vital for planning stable and cost-efficient SAPV systems nationwide. SAPV systems rely on batteries to store excess energy generated during the day, which is then used to power homes and businesses during the night or on cloudy days. Accurate GHI forecasts enable system designers to size the solar panels and batteries correctly, ensuring that the system can meet the energy demands of the users while minimizing the cost.
The GPR model’s superior performance can be attributed to its ability to handle non-linear relationships between the input variables and the output GHI values. GPR is a probabilistic model that can capture complex patterns in the data, making it well-suited for modeling the intricate relationships between weather patterns, geography, and GHI values.
The study’s findings have significant implications for the development of solar power infrastructure in India. With a more accurate forecasting tool, solar power system designers can optimize system performance, reduce costs, and increase the reliability of solar power generation. This, in turn, can help accelerate the adoption of solar energy, contributing to India’s goal of becoming a leader in renewable energy.
The Indian government has set ambitious targets for renewable energy, aiming to generate 40% of the country’s electricity from non-fossil fuels by 2030. Solar energy is expected to play a major role in achieving this goal, with plans to install 100 GW of solar power capacity by 2022. However, the intermittency of solar energy poses a significant challenge to grid stability and reliability.
Improved GHI forecasting can help address this challenge by enabling grid operators to predict and manage solar power generation more effectively. With more accurate forecasts, grid operators can adjust the output of other power plants, such as thermal or hydroelectric plants, to balance the grid and ensure a stable supply of electricity.
In addition to its implications for solar power generation, the study’s findings can also be applied to other fields, such as agriculture and water management. Accurate GHI forecasts can help farmers optimize crop yields and water usage, while also informing decisions on irrigation and water storage.
In conclusion, the study by VIT researchers demonstrates the potential of machine learning to improve solar forecasts in India, paving the way for more efficient and reliable solar power generation. The use of GPR models can help reduce errors in GHI forecasting, enabling the development of more stable and cost-efficient SAPV systems. As India continues to push towards its renewable energy goals, the application of machine learning and other advanced technologies will play an increasingly important role in shaping the country’s energy future.
News source: https://researchmatters.in/news/machine-learning-boosts-solar-forecasts-diverse-climates-india
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