Change is coming, whether we like it or not. As awareness of climate change intensifies, one thing has become remarkably clear, using energy generated by renewable sources is essential to reducing destructive greenhouse-gas emissions and improving the carbon footprint.
While energy sources like wind and solar are increasingly valuable in this effort, they can also be unpredictable and irregular. For example, how much energy can any single panel or solar plant contribute to the grid on any specific day? The answer to that question has previously been complicated.
To further rely on the sun and the wind and make the energy they create a bigger part of the grid, we must be able to understand how much power will be available, what the demand for it is and then use this energy to meet consumer needs as efficiently as possible.
This is where Big Data comes in. Through a brilliant combination of weather data, satellite feeds, predictive analytics and machine learning, we’re entering a future where renewable power can reach the grid on a consistent basis. In fact, the latest forecasting technology may be the missing puzzle piece in the extensive adoption of solar energy.
For example, using cloud-imaging technology and sky-facing cameras to predict the weather up to a month in advance can increase the renewable power stored or delivered to the grid by up to 10 percent, enough to power 14,000 homes.
Big Data Analytics can also be used to detect underperformance and detect when inefficiencies happen without having someone on site. This involves accumulating ground level sunlight-intensity data to signal when panels aren’t performing at expected rates and sending an alert for repairs or maintenance needed.
This kind of analysis can even be used to study whole communities and suggest where solar panels could be the most effective based on sun exposure and weather patterns. South Australia, for example, in a collaboration with Tesla, has built the world’s largest virtual power plant, connecting 50,000 Tesla batteries with panels, reducing costs associated with stabilizing the energy grid by $28.9 million. It's a collaboration that could eventually power the entire state.
The better we can analyse these energy supply-and-demand models, the more a community’s backup energy needs can be accounted for as well. When energy demands are consistently met by renewable sources, companies can reduce their safety margins, which are usually made up of costly and environmentally damaging fossil fuels. This translates to cheaper, more efficient power for both companies and consumers.
Big data and machine learning have already begun to transform many industries and now it's changing the way we think about and use solar energy. Energy companies, consumers and investors who have all heard the plea for change finally have an answer for how they can harness technological advances to become less a part of the problem and more a part of the solution.
For more information or to join our Smart Community Program, call us on 1300 732 679 or Get a Quote today to start your Innovative Solar Journey.