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Harnessing AI for a greener future:

The indispensable role of short-term energy forecasting in renewable integration

The application of Short-Term Energy Forecasting through AI emerges as a transformative solution to reaching net zero.

In the race to combat climate change, nations around the world are rapidly pivoting towards more sustainable energy solutions. Renewable energy sources, such as solar and wind, are at the forefront of this transition, providing a promising pathway to reduce greenhouse gas emissions and limit global warming. However, the shift away from traditional, predictable energy sources to ones that are inherently variable presents new challenges. The unpredictability of renewable energy generation due to natural conditions demands innovative approaches to integrate these sources into existing power grids without compromising stability and reliability. 

The application of Short-Term Energy Forecasting (STEF) through Artificial Intelligence (AI) stands as a transformative solution to this issue. STEF harnesses advanced AI techniques to accurately predict the output from renewable energy sources in the near term, usually within a 0 to 72-hour window. By doing so, it equips grid operators with critical insights necessary to adjust and optimize energy supply in real-time. The precision and agility of AI-driven STEF are particularly vital as the world sets its eyes on important climate milestones, such as the Conference of the Parties (COP28), where the integration of renewable energy sources will be a central topic of discussion. 

Renewable energy's inherent variability contrasts starkly with fossil fuels, which can be burned as needed to meet the instantaneous demands of the power grid. Solar and wind energies depend heavily on the weather and time of day, factors that are beyond human control. A gusty afternoon can generate ample wind power, while a calm day may not. Similarly, solar panels generate maximum output under direct sunlight, which is absent during the night and diminished on cloudy days. The inability to control these factors makes it challenging to match energy supply with demand in the absence of widely available short- and medium-term storage options. 

The crux of integrating renewables into the power grid is managing this unpredictability without leading to power outages or excessive energy wastage. Surplus energy, for instance, is not merely inefficient; it can overwhelm the grid and damage infrastructure, necessitating costly repairs and leading to service interruptions. Conversely, a deficit in power supply risks blackouts, which can have severe economic and social impacts. Thus, a dynamic, intelligent approach to energy management is required—one that can adapt to rapid changes in energy generation and usage patterns. 

STEF has been used to mitigate the variable nature of renewables for decades, over which time new modelling techniques have been developed to enable increased accuracy and support additional features. The trend of growing data availability has driven the development of physical and statistical models of renewable farm production with increasing complexity and accuracy.  

Each step to increase the amount of renewable energy incorporated in our energy systems necessitates an improvement in forecast precision to enable it. Most recently, machine learning and AI models have been introduced to push accuracy to the cutting edge, making use of the large, complex data sets that are available in the context of renewables production. 

STEF, enabled by complex AI algorithms that analyze vast datasets, including weather conditions, historical energy usage, and real-time sensor data, provides an efficient solution to today’s challenges. These algorithms can learn and identify intricate correlations and causations, enabling them to predict energy production levels from renewable sources with remarkable accuracy. This predictive capability allows for precise adjustments to energy distribution, facilitating the effective supplementation of the grid with renewable sources or the curtailment of reliance on traditional energy sources during peak periods of renewable production. 

The advancement of AI in energy forecasting is already showing promising results. For instance, the U.S. National Renewable Energy Laboratory (NREL) has employed machine learning to refine the predictability of wind energy, which has led to marked improvements in grid management and cost reductions. By accurately forecasting wind energy production, grid operators can more efficiently integrate this energy into the power grid, reducing the need for costly and carbon-intensive standby energy sources. 

In Denmark, STEF has long supported the country's world-leading integration of wind energy and as the penetration of wind has increased, the accuracy requirements for forecasts have become more and more stringent. The more recent application of AI has driven forecast technology to a level of accuracy that enables almost half of Denmark's electricity consumption to be powered by wind, showcasing the vast potential of AI in creating resilient, renewable-powered grids. Similarly, Germany's celebrated 'Energiewende' or energy transition relies heavily on STEF to blend a significant proportion of renewable energy into its national grid. 

California's struggle with the "Duck Curve" illustrates the challenges faced by grid operators in regions with high solar energy penetration. The "Duck Curve" is a graphical representation of the mismatch between peak demand and the timing of solar energy production. Energy forecasting helps California manage the steep ramp-up of demand in the evening when solar production drops but demand surges, thereby reducing the need for carbon-intensive peaker plants. 

The application of STEF also stretches to countries with emerging economies. In India, where renewable energy generation is heavily influenced by the monsoonal rains, STEF is critical for managing grid stability. Spain's success in balancing one of the largest wind power portfolios in the world also heavily leans on accurate short-term energy forecasts. 

Despite the promising developments, the journey toward a fully renewable-powered grid is fraught with challenges. One significant issue is the digital divide, which poses a risk of leaving less affluent nations behind. These countries may lack the resources and infrastructure to implement sophisticated AI solutions, potentially hindering their energy transition and, consequently, the world's collective progress towards sustainability. The digital divide not only affects the deployment of technologies like STEF but also impacts the global equity of climate action. 

Addressing this divide requires international cooperation and investment in digital infrastructure and education to democratize access to AI technology. Another critical concern is cybersecurity. As energy systems become more integrated with AI and connected technologies, they also become more vulnerable to cyberattacks. Ensuring robust cybersecurity measures are in place is crucial for maintaining the integrity of energy infrastructures and the trust of consumers. 

The path to a greener future lies in the convergence of technological innovation, international cooperation, and progressive policy development. It is imperative that global investments not only focus on advancing AI capabilities but also on bridging the technological gap between nations and fortifying cybersecurity frameworks. Only then can we leverage tools like STEF to their full potential and ensure a seamless transition to renewable energy systems worldwide. 

As nations gather for COP28, the urgency of incorporating AI-driven STEF into the energy transition strategy is underscored. This technology is not merely a facilitator of operational efficiency; it represents a fundamental shift towards a more sustainable and resilient energy future. 

The imperative for global cooperation and policymaking to harness the full potential of STEF has never been more critical. The collaborative approach required to adopt and implement STEF solutions highlights the broader need for concerted global action in the face of climate change, reinforcing the commitment to a greener and more sustainable tomorrow. Policymakers now have a prime opportunity to leverage the power of STEF, propelling the global journey towards a greener and more resilient energy landscape. 



About DNV

DNV is a market leader in providing sophisticated short-term forecasting solutions. Leveraging AI, DNV's forecasting tools analyze vast datasets, including real-time sensor data from energy-producing assets, to predict energy availability with a high degree of accuracy. These predictions, often made hours to days in advance, enable grid operators to make informed decisions on energy storage, distribution, and consumption. 

To find out more, contact Bea Brailey.



About DNV's Forecaster

DNV’s Forecaster delivers insight into renewable energy supply and demand through reliable, data-driven predictions to help you maximize profits, avoid missed opportunities and optimize operations. Our proven methodology combines industry leading models from the world’s foremost meteorological agencies with an unparalleled understanding of renewable energy systems, plus cutting-edge statistical and physical models, machine learning and data analytics. As a result, our forecasts are globally renowned for accuracy and reliability. 

Find out more about Forecaster here



About Solcast, a DNV company

Solcast is a leading provider of solar irradiance data and forecasting technology, dedicated to bringing accurate data to the market and helping enable the energy industry to deliver the resilient power grids it will take to build a solar-powered future. Solcast customers use irradiance and weather data through the Solcast API in the Planning, Operations, Maintenance and Analytics of over 200 GW+ of assets globally. 

Find out more about Solcast here

12/6/2023 10:00:00 AM

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Beatrice Brailey

Beatrice Brailey

Head of Forecaster