Power Demand Forecasting How to Harness Your Analytics

Power demand forecasting helps modern power utility companies make informed decisions about operations, supply, financial security, and more. Different factors—such as season, temperature, location, and time of year—can impact the ebb and flow of power demand. Rising economic activity can also lead to an increased need for electricity, and taking economic variables into account can significantly improve the accuracy of power demand forecasting.

When carried out correctly, power demand forecasting helps electric utilities make better business decisions based on past trends, current inventory, and future requirements. Understanding upcoming supply chain and inventory needs can also help power utilities prepare for spikes in demand. Armed with this valuable data, organizations have more insight into their customer usage trends products, infrastructure, load switching, and more. 


How Power Demand Forecasting Can Improve Decision-Making

When it comes to accurate power demand forecasting, using the right data is imperative. Analyzing variable data can help utilities make better power demand predictions. These variables can include: 

  • Time: How does demand differ depending on the season or time of day? What are the levels on holidays or weekends?

  • Weather: What is the weather like, including humidity, temperature, precipitation, or extreme weather conditions?

  • Social: What is the current economic or political climate? For example, nationwide COVID-19 shutdowns in 2020 led to more individuals working from home, requiring an increase in power supply to non-commercial properties. 

  • Pricing: How much does it cost a utility to supply electricity to a given geographic area? What does it cost to service a single account? 

  • Past demand: What were the previous electricity consumption levels? How have they changed over time? 

Here are a few ways to use data your electric utility may already have to predict upcoming power demand:

Past and Current Trends

This method of data analysis is based purely on previous power demands during a certain season or period in a specific location. Demographics, the economy, environmental policies, and changes in service or technology aren’t necessarily taken into account when monitoring trends, so this method of power demand forecasting is ideal for short-term projections. 

Artificial Neural Networks

Artificial neural networks (ANN) can learn and adapt using data analytics and metrics. This gives them the ability to make future predictions based on previously gathered information, improving power demand forecasting accuracy by anticipating consumer response and usage. 

This results in more accurate forecast predictions, helping utilities make better decisions about energy consumption. 

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Testing Analysis 

Testing analysis uses an ANN’s predictive abilities to test new data that hasn’t been previously utilized. This method can determine if the particular model should be moved forward as part of the deployment stage. 


Collective Opinion

As the name suggests, the collective opinion uses information gathered from a power utility’s sales team to collect data on past consumer habits and demand. This information can improve processes and predict future consumer demand. 

Sales team members give the utility feedback on customer responses to services, disruptions, manpower, and more. This pinpoints how certain demographics may feel about the utility’s services, helping utilities make better decisions about power demand. 


Get the Information You Need

Power demand forecasting can be complex, and although using the correct analytics can help, there are many other ways to correctly predict industry trends and growth.  Learn more about current customer revenue challenges, security threats, state and self-imposed moratoriums, and more. Watch the UtilityWide Talk: Debt Relief and Revenue Security webinar now. 

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