Solar energy is becoming an increasingly popular alternative to fossil fuels. Households and business owners view it as an accessible and cost-effective option, particularly with the panel cost significantly less expensive than it was several years ago.

However, numerous factors influence how well solar panels produce energy. They include:

• Panel location
• Property latitude
• Season
• Temperature
• Panel orientation
• Shade
• Cloudy days

Many people understandably want clear ideas of the expected results before and after installation. Data analysis tools can provide that, removing much of the uncertainty that may cause customers to delay their solar energy investments.

Measuring the Total Solar Production Per Year

Data analysis tools can verify how solar production impacts all energy usage over time. They enable government decision-makers, energy professionals and others interested in the matter to track solar’s influence.

For example, the U.S. Energy Information Administration (EIA) has charts showing renewable energy usage in the country since 1950. Data released in 2020 indicated a record amount of renewable energy consumed during the previous year. Solar power comprised 9% of the total, with biomass accounting for 43%.

Keeping track of the numbers associated with solar power generation allows analysts to monitor trends. They can also stay informed about the overall solar energy uptake. Using data tools to take a closer look could also involve confirming which states or regions have the largest or least interest in solar energy. That information makes it easier to see where educational outreach, tax incentives or other motivators might boost appeal.

Today’s communities typically use a mix of solar power and energy from other sources. Some people working in the energy sector apply artificial intelligence (AI) to microgrids to provide areas with reliable power in emergencies.

Borrego Springs, California, is heavily dependent on a microgrid, especially since it experiences frequent natural disasters. That microgrid has connections to a solar farm and rooftop to boost its power supply. AI analyzes when to store or distribute that energy based on real-time needs. Besides getting power to all the areas that require it, this data usage could verify how often the microgrid uses solar energy.

Showing the Potential of Pioneering Projects

Many researchers are eager to see how solar energy production could bring multiple benefits, especially for large-scale projects. However, it’s often challenging to attract interest and gain funding for those efforts, especially if they’re unlike what people have tried previously.

Fortunately, data analysis tools can help decision-makers gauge the results before a project begins, letting them measure the expected return on investment before approving it. In one recent example, researchers examined the effects of covering California’s public water delivery canals with solar panels. No projects of this sort have occurred at scale, so analysts knew it was crucial to measure the details of seemingly minor choices.

For example, the typical methods to support solar panels over canals involve using either steel trusses or suspension cables. The researchers determined that choosing suspension cables would give a 20%-50% higher net present value, suggesting that method would give the better return on investment of the two.

The results also showed that every megawatt of solar energy produced could replace 15-20 diesel-powered irrigation pumps. Thus, the team suggested that covering the canals with solar power could directly reduce dependence on known pollution sources.

Confirming the Payoffs of Solar-Based Government Upgrades

Data analysis tools also come in handy for contractors who need to provide updated details about a solar installation as it progresses. For example, many of those professionals use mobile customer relationship management (CRM) interfaces while on work sites. Doing that can cause a 50% increase in user productivity by providing quick information. Whether a contractor needs to order parts, schedule customer appointments or create contracts, a CRM can do all those things and more. 

CRM can also track a solar company’s revenue and customer retention rates, both of which can indicate a competitive advantage in the marketplace. Such data could also determine whether a solar installation business is in an appropriate position to bid for or accept a lucrative but time-consuming government contract.

As many government leaders set ambitious emissions-reduction targets, solar power becomes a feasible way to meet some of those goals. Using it could positively affect surrounding communities as well as energy use, a recent study showed. It highlighted a way to bring solar power to 136,000 homes in Australia.

Researchers compared the solar energy production of 17,000 residential solar panels in Bendigo, Victoria, versus the effects of installing large-scale equipment onto the roofs of Australia’s 21 leased federal airports. The airport project would generate 10 times the solar power, plus offset 151.6 kilotons of greenhouse gases per year.

Tracking Solar Panel Performance During Long-Term Use

People interested in solar energy generally know there are some clear links between power production and certain characteristics. For example, the energy generated will likely decrease on a cloudy day as opposed to one with abundant sunshine.

However, some solar power effects aren’t as easy to spot without data analytics. One study indicated that solar energy could improve rainfall and vegetation cover in the Sahara Desert.

Regardless of how people use solar power, they typically want to know how much average panel performance could decline over time. Using Internet of Things (IoT) sensors could show that, offering yet another data analysis opportunity for photovoltaics.

Researchers recently explored a remote monitoring solution that carries out current-voltage tests to show the lifespan and reliability of existing solar panels. The team envisions utility providers and panel manufacturers using their approach to maximize their gains from solar.

Plus, panels have different energy production capabilities based on their environments. A utility company executive would understandably want details about expected performance in the immediate area, not a city on the opposite side of the country. If IoT data provides more insight into those crucial details, it would be easier for designers to engineer solar panels that are more likely to offer universally satisfying results regardless of location.

Setting Solar Power Outcome Expectations

People can’t predict the future with certainty when it comes to solar energy production. However, these use cases highlight why it’s worthwhile to apply data analysis tools before, during and after installation. That approach can give valuable insights that shape future decisions.