- New research by Terra Quantum shows that quantum machine learning (QML) models can provide significantly more accurate solar power forecasting compared to traditional methods. At the intersection of AI and quantum tech, QML better handles the complexity and variability of weather data that impacts solar output.
- For tackling the problem, Terra Quantum has developed two proprietary Hybrid Quantum Neural Network models that combine classical and quantum components, both outperforming traditional forecasting algorithms by reducing error rates.
- Achieving accurate solar forecasting via advanced QML algorithms helps enable greater adoption of renewable solar energy by ensuring grid stability and predictable returns on investments. Moreover, as quantum hardware matures, the accuracy of these models will continue improving.
The Unpredictability of Solar Power
In line with the Paris Agreement, the global economy must radically reduce emissions to stay within the 1.5°C pathway and the transition to renewable energy sources is critical to achieve these objectives. There is growing demand and need for clean energy as witnessed by global investment in clean energy of USD 1.8 trillion in 2023. However renewable energy sources are inherently intermittent in nature. Traditional photovoltaic (PV) power forecasting methods struggle with the variability of solar output, leading to challenges in forecasting accuracy and grid stability.
The challenge of solar power forecasting lies in the unpredictable nature of weather and its direct effect on solar energy generation. Rapid shifts in weather conditions, such as cloud cover and atmospheric disturbances, lead to variability in solar output, making accurate prediction difficult. This unpredictability requires sophisticated algorithms capable of processing large volumes of data and accounting for complex weather patterns, often with limited historical data. Moreover, the computational intensity needed to analyze this data and provide reliable forecasts can be prohibitive. As the installed power capacity of solar PV is set to overtake coal by 2027, the need for advanced forecasting techniques becomes increasingly urgent.
New Ways for Addressing it
Terra Quantum, in collaboration with HAKOM a company specialized for over 30 years in time series prediction for energy companies, has published new groundbreaking research showing that at the intersection of hybrid quantum computing and AI, quantum machine learning (QML) can provide more accurate and efficient solar forecasting to support the sustainable power transition.
The study introduces a suite of solutions centered around hybrid quantum neural networks, leveraging 2 powerful proprietary hybrid QML models, combining classical and quantum components designed to tackle these complexities. Across multiple configurations, the quantum-enhanced ML models outperformed traditional algorithms and even performed better when trained on limited datasets, showcasing QML’s ability to capture intricate relationships within noisy meteorological data as well as other data-scarce scenarios.
The first proposed model, the Hybrid Quantum Long Short-Term Memory (HLSTM), predicts up to 1 hour ahead and surpasses all tested models by over 40% in reducing error rates. This is impactful for dynamic recalibration of grids on a short term basis based on weather changes.
The second model, Hybrid Quantum Sequence-to-Sequence Neural Network, can predict photovoltaic power into the future for selectable longer term horizons: necessary for long term planning, with 16% higher accuracy and without the need for prior meteorological data. The study which can be accessed here in its entirety, provides a blueprint for understanding and utilizing QML as a tool to enhance renewable energy forecasting.
Why accurate forecasting matters?
Accurate forecasting is pivotal for the adoption of renewable energy as it ensures grid stability by allowing for precise anticipation of power generation fluctuations. Accurate predictions facilitate more efficient use of resources, bolster investor confidence through predictable returns, and aid in policy formulation for renewable energy incentives. This efficiency and reliability in forecasting make solar energy more economically viable, fostering greater investment and integration into the energy mix, thus accelerating the transition to a sustainable energy future.
As Quantum hardware matures, our already performant models will only improve in their capabilities, thus future proofing organizations.
Driving the Development of QAI
Terra Quantum is leading the next revolution in Quantum AI. Quantum machine learning algorithms, due to their probabilistic nature and capability to navigate exponentially larger search spaces, can deliver more accurate results, generalize better with less data, and offer solutions to complex problems like weather-dependent time series forecasting.
We are not only focused on developing cutting-edge solutions and research at the intersection of quantum and AI, but also on empowering our customers with easy and democratized access to our powerful quantum tools. In light of this, we are actively furthering the development of the first low-code QML framework, TQml, accessible via our QaaS platform TQ42, enabling developers and data scientists to build, run, tune and deploy hybrid quantum machine learning models, by interacting with the various types of HQNNs, tuning their parameters and comparing results in a friendly and intuitive UI.
"Our innovative hybrid quantum models are not just theoretical constructs but practical tools that significantly enhance the accuracy and reliability of solar power forecasting," said Markus Pflitsch, CEO of Terra Quantum. "This advancement is a pivotal moment in our journey towards a sustainable energy future, marking a substantial leap over traditional forecasting methodologies”
Energy companies can utilize these advanced hybrid quantum machine learning models to optimize renewable assets efficiently. This technology promises to enhance weather-dependent forecasting, a critical component for renewable energy management by improving efficiency and enhancing adoption. This leads to direct cost savings as well as potential for additional revenue generation through better pricing. While this study focused specifically on photovoltaic power, our models are applicable to wind energy forecasting as well.
Time series prediction is a critical component across industries for problems such as energy forecasting, demand forecasting, predictive maintenance, anomaly detection, sustainable building management etc and we have found that QML is especially well suited for such tasks.
Reach out to learn more about how Quantum AI can help increase efficiency in your business and join the quantum revolution.
For more information on the methods used in study, access the publication here: Photovoltaic power forecasting using quantum machine learning