Predictive models are used for just about every industry these days to help companies take datasets and figure out what may be the right next step for their business. Predictive analytics use historical and current data, combined with algorithms and machine learning, to model unknown future events.
This can answer a wide array of questions from what machines need maintenance to what product should be promoted. This method of analytics falls true for the energy industry too. Here are four types of companies in the sector that you could start with predictive analytics.
Predictive analytics has come in handy for electric providers to be able to better address current and future issues with their electricity infrastructure roadmap. Compare electricity NSW allows for customers throughout New South Wales to look at what their potential monthly electric bill may be and choose an energy provider by using data sources based on their research. Analytics software can help to forecast any potential long-term issues for providers.
With predictive models, electric providers can monitor outages and use historical data to determine if this is a recurring issue for this particular region of their energy service. New data can also raise alerts for a modern electricity system, giving providers an advantage in customer service by letting consumers know what the issues are in real-time.
Some predictive analytics companies have come in handy for another utility, gas. Energy storage and distribution recently made headlines following major snowstorms in the U.S., with several homeowners left in the dark and in the cold due to poorly maintained infrastructure and preparation for winter weather.
Data visualization and machine learning could have allowed for an energy provider to prepare their workforce to deal with any potential outages and provide greater warning to customers.
By keeping the gas flowing, predictive analytics can be used to help understand energy cost, and give homeowners and business owners get a better grip on their heat bill. Companies may use data points to understand peak hours and peak times, looking for any anomalies that could bring about necessary reforms to the system.
3. Solar Energy
With the cost of electricity being on the rise in some areas, more homeowners and business owners are turning towards renewable energy sources to reduce their electric bills and their carbon footprint. Predictive models have become useful for solar power providers to get a better understanding of energy efficiency for solar panels in certain regions. After all, the positioning of panels is what helps convert the sun’s rays into electricity.
An analytics platform allows for renewable energy providers to also monitor developments in this growing market, seeing where they can attract more attention from prospective customers.
These advanced analytics can also be used for greater marketing purposes, driving the need for a potential social media campaign centered around creating larger renewable energy zones, and promoting the benefits of going green.
4. Wind Energy
Predictions can help prepare for any business problem and raise business performance almost instantly. Data science can be used by wind energy providers, creating dashboards to give renewable energy users a better understanding of their savings and how to anticipate their energy reserves.
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With data preparation, wind energy providers can also determine the best regions to establish turbines to better generate for a growing marketplace.
The predictive analytics process is of great help to a new industry, taking historical data from providers for other energy variations to get an idea of where customers have help issues and how they can better attract them to renewables. Whether it’s through the use of sensors or a spotlight on advanced technologies, this enterprise application could be just what some homeowners and business leaders are looking for.