Energy needs don’t always align with expectations. But predictive analytics is helping companies reduce their energy footprint and improve forecasting of how much power they will need at a given time. With data-driven tools, companies gain a more granular look at their energy use.
“Sometimes, what they think is going on and what’s actually happening are two different things,” Abiodun Iwayemi, engineering manager at the nonprofit Efficiency Vermont, which works with companies to analyze their consumption patterns, told BizTech earlier this year. Exceeding or falling short of energy predictions can prove especially costly for energy providers themselves.
Making better forecasts begins with predictive analytics, which can identify the likelihood of future events based on analysis of historical data. It’s a discipline that has been in practice for decades. But today, energy providers are deploying predictive analytics using data mining techniques and machine learning capabilities to help forecast and meet future energy needs.
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Forecasting Supply and Demand Is Essential for Energy
Between changes in weather, occupancy, foot traffic, business workloads and more, consumer energy needs can fluctuate significantly. This makes it difficult for energy providers to ensure supply will meet the demand. For example, if an energy provider generates more energy than needed, it has wasted time and money. Conversely, if it fails to meet consumer demand, customers can lose trust in the provider.
That’s why many energy providers are turning to predictive analytics to draw from dozens of variables and stay on target. These analytics, according to a 2022 report by Popmodo, help forecast consumption and improve a company’s chance at achieving their sustainability goals.
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How Predictive Analytics Plays a Role in Forecasting
Predictive analytics uses data on what has happened in the past to make highly educated guesses about what is likely to happen in the future. More specifically, and often through the use of statistical models, machine learning algorithms and other data analysis techniques, predictive analytics finds patterns in historical data to identify risks and opportunities and forecast potential scenarios. In this way, predictive analytics helps drive strategic decision-making.
For energy companies, trying to forecast without predictive analytics can be particularly daunting due to the numerous variables at play, including energy sources, weather conditions and inconsistent workloads. Predictive analytics efforts greatly improve the accuracy of these forecasts, especially when they involve regression analysis.
Regression analysis is a mathematical way of sorting factors, such as which variables matter most, which can be ignored, how variables interact with each other and how certain you can be about all the variables. It works by determining the relationship between two or more variables. These relationships are written as a mathematical equation that can help predict the outcome should a variable change.
“Regression allows us to gain insights into the structure of that relationship and provides measures of how well the data fits,” says Janice H. Hammond, a professor of manufacturing at Harvard Business School, in a Harvard Business School online article.
“Such insights can prove extremely valuable for analyzing historical trends and developing forecasts.”
Using Predictive Analytics Solutions to Forecast Energy Needs
Energy and utility companies looking to take advantage of predictive analytics can deploy tools from vendors such as Amazon Web Services and IBM. For example, using machine learning, Amazon Forecast is already helping energy providers accurately anticipate and meet the needs of their customers.
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Further, IBM’s Planning Analytics Workspace allows users to forecast with automated tools that model time-dependent data, making it easy to make predictions even if they are unfamiliar with time series modeling or regression analysis.
By forecasting supply and demand with predictive analytics, energy providers can see significant savings of time, money and other resources.
