It is no longer news that we are witnessing a global revolution in the use of Big Data to improve efficiency in the energy industry, healthcare, security, social media and other sectors. Over the years, environmental issues such as climate change, has attracted a lot of attention and has been widely discussed, the same story can be told about Big Data and analytics. Big Data analytics, Machine Learning and Artificial Intelligence are currently three strong forces across the global economy. Organisations that are sophisticated enough to leverage these tools are developing critical competitive advantage based on their nuanced insights and accurate forecasts.
The application of Big Data and predictive analytics in the Renewable Energy industry has not been as widespread as in other industries. One of the main reasons for this could be attributed to lack of large datasets available to analyse. However, there are some current applications which include predictive maintenance, asset performance management and automated deal sourcing all leveraging different analytical tools and sources of data. Embracing these trends will allow investment funds to refine their processes and boost investor returns, thereby providing a key differentiating factor between competitors. This article concisely analyses some ways in which Big Data is changing the future of the renewable energy sector. They are discussed as follows:
Streamlining Operation and Maintenance Processes
The construction of large-scale wind and solar powered plants has resulted in higher energy production globally. However, one obstacle is how to maintain the power plants spread across different terrains. For instance, a massive solar powered plant contains hundreds or thousands of solar panels, different types of sensitive equipment, sensors, installers, inverter systems and a complex web of wires. As a result, basic ground level maintenance become increasingly difficult, affecting the daily energy output of the plant.
With the help of Big Data analytics, companies can streamline their operations and management processes to a great extent. For example, one of the largest self-storage management companies in the U.S by name Extra Space Storage (ESS) is already using Big Data to run day-to-day operations of various solar panels. The company has used Virtual Irradiance (VI), a solar management program based on Big Data that collects ground-level irradiance (sunlight intensity) data accurately. The program eliminates the need for expensive on-site sensors thereby saving a huge amount of capital.
The analytics tool has enabled ESS to verify how well solar panels perform under varying climatic or weather conditions compared to their ratings. The software can send signals whenever solar panels underperform or over perform. Thus, the ground crew can locate the problem quickly and take appropriate actions. Hence, they are now better equipped to handle ESS’s increasing solar fleet, while reducing operations and maintenance costs considerably.
Predicting Weather Conditions Based on Historical Data
Undoubtedly,one of the biggest advantages of Big Data in the solar industry is the optimization of the energy production and its distribution. Though renewable energy generation is on the rise, the intermittent and unpredictable resources (sunlight and wind) often hamper the overall renewable energy production. Consequently, it becomes difficult for solar and wind power plants to operate at their maximum potential.
The good news is Big Data is rapidly changing this scenario. Historically, solar and wind power plants have always collected data. In this era of Big Data, predictive analytics and machine learning, this data can now be combined with weather and satellite data. In short, this solar and wind forecasting technology can predict weather conditions well in advance, allowing renewable plants to increase their production significantly.
Instead of increasing the number of solar panels or wind turbine, the idea is to increase the efficiency of existing plant infrastructure. For instance, IBM’s Hybrid Renewable Energy Forecasting (HyREF) uses a combination of Big Data, predictive analytics and weather modelling technology to predict the variable resources for wind and solar power production. It can increase the amount of renewable power generation integrated into the grid by 10%. This additional energy can power 14,000 homes! Thus, power plant owners no longer need to spend more money on additional infrastructure costs.
Reducing Renewable Energy Production Costs
Experts believe that renewable energy is enjoying a rising support from private organisations and individuals due to the gradual decline in its production cost. According to figures released by Bloomberg’s New Energy Finance, the price of building an offshore wind farm has fallen 22% in 2016 across Europe. From 2012 to 2016, the cost fell by almost 46%. At present, erecting turbines in the seabed costs an average $126 per megawatt-hour capacity compared to $155 per megawatt-hour price for new nuclear developments across Europe.
As a result of the latest Big Data and predictive analysis technology, renewable energy companies can now produce more energy without yielding additional infrastructure costs. The ever-growing ability to extract useful information from Big Data is one of the reasons behind the gradual decline in the renewable energy prices. It is predicted that by 2030, renewable energy will be cost competent with its conventional counterparts.
Making Renewable Energy Projects more Bankable
Though renewable energy sector is growing, the growth rate could be much better. Unfortunately, at the moment, only a handful of financial organisations, banks and insurance companies consider renewable energy projects as credible investment options. Usually, the lack of reliable data that supports the long-term viability of a project is the reason why most investors are hesitant to back a renewable energy venture.
Another obstacle is the risk based on energy output volatility. Though the Power Purchase Agreement (PPA) can guarantee process for power production from a solar facility, it cannot shield investors from the risk of energy output volatility. With Big Data tools, companies can forecast energy generation based on past performance, weather and other parameters accurately. It can also help determine the precise quantity of wind turbines or solar panels required to produce the desired output. Thus, businesses can use Big Data to make renewable energy projects bankable.