The rise of distributed energy resources (DERs), such as solar panels and wind turbines, is transforming the energy landscape.

As a product development agency deeply involved in the utility sector, we’ve observed firsthand how these changes affect energy demand forecasting and capacity planning. Integrating DERs into the grid presents exciting opportunities and formidable challenges for utility companies.

From Predictable to Variable: A New Energy Demand Forecasting Era

Historically, utilities have relied on base load and on-demand energy sources like hydro, nuclear, coal, and natural gas. These traditional methods allowed for the predictable generation and distribution of energy.

However, DERs like wind and solar energy are intermittent. We can’t simply “turn them on” when we need them. They generate power only when environmental conditions are favorable – when the wind blows or the sun shines.

This fundamental shift requires utilities to rethink their energy management and demand forecasting approach.

The Delicate Balance: Precision in Energy Demand Forecasting

Managing the grid with DERs is like performing a complex dance — one wrong move can ruin the performance.

Utilities must balance base load and peak demand while incorporating intermittent energy sources. This balancing act makes accurate energy demand forecasting more critical than ever.

It’s a high-stakes performance with severe consequences for missteps. Too much power on the grid overloads the system, and too little leads to blackouts. The margin for error is razor-thin.

The Customer Behavior Conundrum: A New Variable in Energy Demand Forecasting

DER integration is also altering customer behavior and expectations. This shift introduces new variables into the energy demand forecasting equation:

1. Bidirectional Power Flow

With the increasing adoption of residential solar panels and other DERs, energy flow is no longer a one-way street. Customers are now both consumers and producers of energy, feeding unexpected excess power back into the grid.

2. The Electrification Wave

We’re witnessing a significant shift from natural gas toward electric alternatives.

The electrification of everyday life, from home heating systems to stoves and vehicles, is driving up demand. This trend, coupled with population shifts to regions with high energy needs (imagine how many air conditioning units are installed in Florida each year!), reshapes load patterns across the grid and complicates energy demand forecasting.

3. Heightened Energy Consciousness

Today’s consumers are more aware than ever of their energy usage and its sources. Many demand “green” energy options, but they want these renewable sources to be reliable and cost-effective. For instance, solar and wind are unpredictable and expensive.

This creates a challenging balancing act for utilities, which must incorporate these preferences into their energy demand forecasting models.

The Data Imperative in Modern Energy Demand Forecasting

Data is king in this new era of energy management.

Utility companies need access to granular, real-time data from every point on the grid to make informed decisions and improve their energy demand forecasting capabilities.

This level of insight is crucial because:

• Minute-by-minute data allows for more accurate predictions of energy generation from DERs. For instance, how much energy will a particular solar farm provide at any moment?
• Real-time insights help utilities determine where to send excess energy or when to tap into alternative sources based on forecasted demand.
• Detailed data can reveal opportunities to sell surplus energy in the open market — a key consideration in comprehensive energy demand forecasting.

However, the sheer volume of generated data presents its own challenges.

Utility companies must capture this data and have the infrastructure to process and analyze it effectively. Sophisticated energy demand forecasting requires advanced data management and analysis capabilities — in other words, costly technology, infrastructure, and manpower.

Thankfully, a less resource-heavy alternative exists.

The Role of AI and Machine Learning in Energy Demand Forecasting

Nobody has data like energy companies. As such, they’re uniquely positioned to leverage artificial intelligence (AI) and machine learning (ML) technologies to forecast energy demand and capacity.

AI and ML can help utilities:

• Integrate disparate data points, from meteorological forecasts to customer usage patterns
• Identify trends that humans might miss, improving the accuracy of demand predictions
• Support real-time decision-making on asset deployment based on forecasted energy demand
• Guide future investment strategies based on historical patterns and predictions of energy demand

By harnessing these technologies, utilities can transform raw data into actionable intelligence. This enables more efficient DER management within the broader energy ecosystem and enhances energy demand forecasting capabilities.

Shaping the Future: Beyond Traditional Energy Demand Forecasting

While accurate forecasting is crucial, forward-thinking utilities look beyond prediction to shape energy consumption patterns. Once you have some expectations of your DER fleet’s energy generation capacity and your customers’ usage behavior, you can incentivize customers to use their energy in a way that optimizes your fleet’s existing energy generation.

Some effective approaches include demand response programs and time-of-use rates, which incentivize customers to reduce energy use during peak times.

These strategies reduce utilities’ and customers’ costs while improving grid efficiency and energy demand forecasting accuracy.

The Path Forward in Energy Demand Forecasting

Utility leaders who want to position themselves for success must take a multifaceted approach to enhancing their energy demand forecasting capabilities.

Begin with a comprehensive data assessment. Evaluate your existing data collection and analysis capabilities. Identify gaps and areas for improvement.

Next, perform a value stream mapping assessment to understand what drives value and cost within your operations. This will help you identify the metaphorical levers you can pull to impact those variables.

Then, determine what decisions are key to your DER strategy and what data is needed to confidently inform those decisions.

Based on your needs, create a plan to augment your data infrastructure and analytics capabilities. Look for opportunities to leverage AI and ML technologies to enhance your forecasting and decision-making processes.

Also, engage with customers. Develop programs and incentives that align customer behavior with grid optimization goals.

Finally, stay agile. The energy landscape is evolving quickly, so leave room for flexibility in your strategies. Be prepared to adapt your energy demand forecasting techniques as new technologies emerge.

Final Thoughts

At Method, we help utility companies maximize their data and technology investments. We leverage our data assessment, value stream mapping, and user experience design experience to develop tailored strategies that drive real results.

As the energy sector evolves, one thing is clear: the utilities that will lead the way are those that harness their data to create efficient, resilient, and customer-centric energy systems.

The future of energy demand forecasting is distributed, data-driven, and dynamic — and Method is here to help you make the most of it. Contact us today.