Solar’s Growth Depends on Data
With the world in 2020 being even more topsy turvy than normal, industries, and even human beings are all, on the lookout for reliability. Reliability of data means reliability of assets being managed which leads to reliability of accurate generation forecasts which in turn leads and reliability of monitoring and reporting which in turn leads to reliable rates of return for Solar power producers and their investors.
There is no need to dwell that energy costs are now lower with Solar than other traditional power sources. It’s exactly why the industry has spread so quickly to parts of the world like India without traditional government supports. So what is one of the most crucial factors in making sure that there is an increased uptake of solar? The answer to that may be reliability of data.
So why reliability?
Reliability leads to all of the above working as it translates into reducing risk which in turn leads to and increased profitability. As solar plants grow in size and additions of storage a matter of when not whether, data needs will keep becoming more complex.
Simply put, unpredictable readings due to weather, intermittent sunlight, seasonal and geographical changes, as well as age, type of panels as well all other equipment in a plant can lead to solutions that aren’t entirely dependable. After all modern solar plants are a jigsaw puzzle of parts from various manufacturers. Minimising unpredictability by making sense of chaotic data for optimization of solar is where Digital Twin technology with AI really add value. When the technology can ensure that all these variations are accounted for and managed with the construction of the plant’s digital twin, then solar’s capacity will be completely optimised.
What does ‘reliability’ mean in terms of data?
Underperformance is a prevalent problem in the solar world. While assessment of underperformance seems easy, in theory, it relies very heavily on analysing data from multiple streams and then comparing this against surrounding panels.
So when it comes down to it what’s required?
1. A model should be able to work with data from various sources. After all solar measurements may show a variety of errors. Some of these include shading, dirty sensors or instruments which may be mis-calibrated on mis-aligned. If different sources are in not aligned in calculating where the problem may be, there should be clear systems to ensure accuracy in how the data is represented.
2. Next this ground level data needs to be combined with models which gather robust and accurate data from all kinds of weather conditions and providers so all the collected data can be trusted. Historical data, real-time updates and accurate forecasting are all equally important here.
3. Ensuring that models are reliable means that all the solar components need to be validated. This will ensure accuracy of the data on the basis of which crucial decisions can be made.
4. Add to this, speed of delivering the data is key since this leads to better solutions.
5. Quality screening data is another important step so that the typically high margin of errors can be minimized.
Let’s look at how the reliability of data increases the efficiency of solar solutions
To illustrate, let’s look at an example from one of our customers in India. In India though the government has set significant penalties for inaccurate forecasts, enforcement is quite lukewarm meaning adoption too is spotty. However, one of our customers who were working with energy exchanges realized that the price they received per MWh was worth much more if the amount was very accurately produced, than if it was inaccurately produced. They were able to maximize their returns using our multi-stage ensembling which uses data from multiple weather providers, historical data and the deployed Digital Twins of the plant to ensure very accurate forecasts which helped our customer.
Results like these show how AI and ensuring reliability of data can increase efficiency with reliable results and it also illustrates the need to invest in data optimization. What we can ultimately see here is how a marriage of trustworthy data and solar solutions can lead to a brighter future for the solar industry.
With AI insights from Sharat Singh, CEO at Quadrical Ai
Author: Kitty Chachra, CRO at Quadrical AiShare on Facebook Share on Twitter Share on Pinterest
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