Issue 01
The State of (power) Play: Energy Justice and AI
Dear Reader,
Late last year, the International Energy Agency (IEA) published an article characterising energy and AI as the next ‘power couple’ — a call out to the increasingly complex energy grids of the future that require more advanced analytical tools to manage. Given the hype around the potential of AI, this coupling might be unsurprising, but how challenging is this integration considering the many factors that complicate energy systems and transitions?
The energy sector remains the largest global contributor to greenhouse gas (GHG) emissions. With fossil fuels like coal, oil and gas responsible for more than 75% of global GHG emissions, the transition to cleaner sources of energy is urgent. And despite the region’s renewable energy growth, rising electricity demand continues to drive up coal emissions. Digitalisation and power-hungry data centres will only add to this demand for electricity.
As Asia navigates this complex transition, the economic and human costs loom large, with energy transitions projected to cost $5.8 trillion annually in 48 developing economies alone. Phasing out coal can lead to significant job losses, as observed in China and Europe. Amidst these challenges, there is a growing push for smarter energy grids, with varying levels of automation.
While AI promises to enhance efficiency and predictability in energy management, its adoption must be tailored to Asia's unique challenges, particularly in countries where energy access is still insufficient. The path to AI adoption also needs to solve challenges around privacy, cybersecurity and lack of AI explainability.
Read on to find out more about AI’s role in sustainable energy futures for the region.
Curated reads
Research on approaches and frameworks highlighting key challenges, solutions, and opportunities in adopting AI across the energy system. Find definitions to some key terms in our climate glossary.
Synthetic data could address core challenges to using AI in energy systems
Given the critical nature of energy infrastructure, trust is key to AI adoption for stakeholders across the energy system. While large, diverse datasets enhance the reliability of energy AI, collecting sensor data can be costly, raise privacy concerns, and may have gaps. To address these issues, researchers propose using synthetic data in the energy sector.
Researchers suggest that synthetic data can improve Trustworthy AI by creating transparent, unbiased datasets that lead to fairer models while promoting sustainability by reducing the need for resource-heavy real-world data collection.
Using synthetic data does have its challenges, however, including in assessing its reliability and determining which real data is needed to create valuable synthetic data. For South and Southeast Asia, which have the highest number of households that do not have equitable access to energy, gaps in ground data will determine how reliable derived synthetic data is.
Solving for the efficiency & privacy trade-offs in smart meters and smart grids
As Asia moves toward grid optimisation in smart energy grids, data generation will rise, making privacy a topical concern. The use of granular data from real-time monitoring of consumers in smart meters has the potential to reveal personal details like appliance use, household size, and daily routines. While strong policy and regulatory frameworks are needed to protect privacy while pursuing energy efficiency goals, researchers have also examined ways to protect consumer privacy in smart meter usage and data.
The authors of this article undertook a survey of privacy-preserving techniques used in smart meters (SM). Some of these techniques include altering SM data through cryptography, adding random noise to the data, and managing demand-side energy storage. There are some limitations with these methods; de-anonymisation is still possible and these compute-intensive protections are expensive, requiring more research and investment to strengthen solutions.
Predictive cooling in the tropics
At the consumption stage, rising temperatures mean more AC usage, particularly in commercial buildings, straining the energy grid. In what could be a boon for sustainable and efficient cooling in tropical climates, researchers have shown how advanced deep learning models can predict next-day cooling needs for these buildings. They show the advantages of sequence-to-sequence models in knowing what data to keep and what to discard. And how, by using historical data, these data-driven models are easier to develop than more complex physics-based models.
Some limitations of these models include privacy concerns stemming from the need to monitor building occupancy as crucial data for predicting energy use. Other challenges to overcome include gaps in data quality, quantity, and interpretability. For sustained adoption, interpretability of models is needed to increase trust and accountability in smart energy systems, as this research article on Explainable AI for energy found.
AI-enhanced building energy management in challenging urban environments
In many Asian cities, extreme environmental conditions, unstable power grids, rapid development, and limited historical data require new approaches to creating sustainable and efficient energy systems for buildings.
This article proposes a physics-based model to leverage the best principles of both machine learning and physics for forecasting in energy systems. As traditional data-driven building energy forecasting models are hard to generalise (i.e., the ability to make accurate predictions about unknown data based on limited training data), they argue for using a physics-based model that predicts building energy usage and a lumped-parameter model to predict how buildings will handle heat. A Domain Adaptation technique, a type of Transfer Learning that uses data from similar buildings to reduce reliance on historical and labelled data, is added to tweak pre-existing models to work in new situations.
Combining these techniques predicts future data points not included in initial measurements by converting simulated physics data into estimates for unobserved periods.
The Ethical Turn Towards AI for Energy Justice
Even as we focus on technical AI solutions for energy management, it's crucial to consider the broader societal impacts of these technologies. Given the context of inequities in energy access in Asia, adoption of AI in the sector should seek to reduce, not deepen marginalisation. To ensure equity in the energy system, researchers argue that energy justice principles should inform the selection of data, algorithms, models, and metrics in the ‘AI for Energy’ lifecycle.
Energy justice is a framework to ensure fair and equitable access to affordable, reliable, and clean energy resources. The choice of AI techniques, such as centralised or decentralised control, can impact how communities access and control energy sources. One example is Reinforcement Learning, which can adapt to changing environments, allowing for greater control over localised data. However, poor interpretability of system behaviour affects its accountability, leaving room for future development.
On our radar: Decentralised approaches to energy systems
Decentralised renewable energy systems, such as solar microgrids, are poised to expand significantly due to their resilience to disruptions, enhanced reliability, and price stability owing to the localised, and community-governed nature of energy generation. Decentralised systems are small energy networks creating power independent of, or to supplement energy from larger grid systems. In Japan, for example, microgrids are a key feature of smart and resilient cities in disaster-prone regions.
Here’s a look at new mechanisms and indicators of change in Asia, with a special highlight of AI’s role:
Microgrids
Despite increased interest and funding, microgrid projects have trouble taking off. Small microgrids struggle with demand variability and renewable energy volatility. Across India, solar microgrid projects fall into disrepair due to a lack of local maintenance capacity in communities. Failed projects are seen by residents as “fake energy,” not meeting local needs, and creating toxic waste as defunct equipment degrades over time. In Indonesia, public-private funding models fizzled over time, failing to account for short infrastructure lifespans and high maintenance costs.
Despite these hurdles, renewable microgrids hold potential due to the low cost of solar panels, improved battery technology, and suitability in tropical climates in the Asian region. Emerging integrations of AI to address barriers in microgrid implementation are nascent yet promising and could potentially improve the sustainability and success of these projects.
Energy communities
Energy communities are characterised by local ownership, collective decision-making, and distributed economic and social benefits from energy sources and are shaped by localised needs and demands. For example, in Malaysia, the local workforce supports the electrification of rural areas by installing, managing, and maintaining solar plants in their villages. For the most part, however, Asian countries still have a way to go in creating a regulatory environment that permits private entities to generate, manage, and trade in energy.
With the introduction of smart grids and AI tools, communities have the ability to forecast their energy needs, categorise consumers, control loads, and examine data to gain insight into their energy consumption patterns. This bottom-up approach to community-driven governance along with local funding models could enable inclusive and just energy distribution, making the implementation of energy policy more effective. It could generate jobs and revenue as community representatives become more involved in energy management and trade in surplus energy.
Around the web
A series of ground reports by Scroll on the human cost of energy transitions in India.
A cheat sheet from MIT on the major greenhouse gases driving climate change.
Image Carbon offers a way to calculate the carbon footprint of your website along with ways to reduce it.
Low Tech Magazine has a new book series, How to Build a Low-tech Internet, on their solar-powered website! It considers the historical context of digital technology and its impact on sustainability.
Coldplay’s Tour Emission Update for 2024 and Massive Attack’s latest festival gig lead the charge on green-ing the music industry.
Our second podcast episode is out! Listen to Cathy Richards and Izni Zahidi as they discuss the applications of Geospatial AI for climate action, trends in democratisation of map-making, and the ethical risks arising from the use of GIS data.
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