Unlocking Renewable Energy with AI and IoT
Our planet’s energy infrastructure needs to grow faster than it has since the first arrival of electricity: global demand is predicted to grow by at least 3.4% a year, driven by mass electrification and digitalisation. The International Energy Agency (IEA) estimates that the world must add or replace 80 million kilometres of power lines, the equivalent of all the power lines that exist today, by 2040 to be able to manage this surge.
This enormous rebuilding project isn’t just an engineering challenge; it’s a race against time, especially as energy demand is being reshaped by data centres, electric vehicles and digitised economies across Asia and beyond.
To further complicate matters, solar power is being adopted at a record pace, following pledges from global leaders to triple their collective renewable energy capacity to at least 11,000 GW by 2030. While this signals progress against climate change, the pace of deployment magnifies a fundamental technical weakness: intermittency. To ensure grids remain stable while we pursue net zero goals, we must learn not just to deploy more renewable capacity but to manage it closely around the clock.
That’s exactly where artificial intelligence (AI) and the Internet of Things (IoT) are proving transformative. No longer experimental, they are already helping companies cut energy costs, boost resilience and lower carbon emissions while making every sun electron count.
The rise of intelligent renewables
AI is helping to combat one of the greatest historical barriers to the adoption of renewables: unpredictability.
By processing high resolution weather data, learning local micro-climates and running physics-informed neural networks, AI improves real-time forecasts for solar irradiance. According to the International Renewable Energy Agency, these measures can increase forecast accuracy by up to 30%, making renewables more and more competitive.
AI-powered weather intelligence also helps grid operators and energy traders better manage supply-demand balance, reduce curtailment and lower reliance on fossil fuel backup. One Fortune Global 500 energy operator is already using AI to manage its considerable portfolio of renewable energy assets spread across multiple geographies. By connecting disparate systems and applying predictive analytics, it improved energy volumes, reduced downtime and streamlined the company’s participation in energy markets. The result: a return on investment estimated at 8-10 times its cost, driven by improved asset performance, operational efficiency, and smarter trading strategies. AI doesn’t just monitor data but also multiplies the impact of every kilowatt generated.
AI also helps grid operators, a crucial partner in energy projects. When cloudy weather threatens to impede solar production, intelligent systems can use the forecast to pre-emptively re-dispatch flexible gas units, spin up battery storage or call on demand-response fleets. That proactive balancing reduces the need for fossil-fuel ‘just in case’ reserves, letting more variable renewables run at full tilt. This real-time responsiveness transforms renewables from passive contributors to active participants in grid reliability.
Put simply, AI isn’t simply enabling more renewables to be used, it’s making them smarter, cheaper and easier to scale. As grid complexity increases, that intelligence becomes essential.
Boosting efficiency with AI for energy
Generation is only half the story. AI is equally valuable on the demand side, squeezing more value out of every clean kilowatt produced.
From smart homes to commercial buildings, AI-driven control platforms sample millions of sensor points including temperature, occupancy and asset condition, and issue micro-adjustments to HVAC, lighting and industrial equipment in real time. Because the software ‘learns’ building thermodynamics and user comfort thresholds, savings increase automatically without manual interventions.
Retailers, real-estate companies and manufacturers are already seeing results. One leading European insurer reduced energy consumption by 36% within one month of installation by using AI to manage energy across its properties.
Meanwhile, a global commercial property group, managing mixed-use buildings, saw a 16% reduction in energy use, achieving full return on investment in under four months. In both cases, savings were achieved without installing new hardware, just by turning data into decisions.
For more intensive industry sectors, AI can fine tune variable-speed drives, kilns and refrigeration systems to avoid coincident demand spikes that typically occur when clouds pass over a large solar array. This prevents unnecessary strain on the grid and lowers peak demand charges, a major cost factor in industrial operations.
By timing non-critical loads to match with local solar output, factories increase the share of carbon-free power in their mix while sidestepping demand charges.
AI-powered microgrids boost self-sustainability
Climate-driven weather extremes and increasing grid congestion are pushing commercial and public sector entities to adopt AI-controlled microgrids, miniature power systems that can run in tandem with, or separately from, the main grid. At their heart lie solar arrays, battery storage and smart inverters linked by real-time optimisation engines.
One example is a European supermarket chain facing both grid bottlenecks and volatile wholesale prices. By installing rooftop solar, a car-park canopy array, 2 MWh of lithium-ion storage and an AI orchestration layer, the retailer can now avoid grid usage during midday solar peaks; it arbitrages excess generation into evening demand and provides ancillary services back to the utility. The result: reduced bills, fewer blackouts and a measurable cut in Scope 2 emissions.
Across the world, microgrids are becoming a foundational strategy for energy reliability, especially in rural areas where traditional grid upgrades are slow or even infeasible.
Microgrids unlock financial value as well as creating independence. In deregulated markets, AI aggregates spare solar capacity across campus microgrids and bids that virtual power plant (VPP) into frequency-response and reserve markets, a practice increasingly enabled by deregulated market access and advanced forecasting tools. Early movers in these practices are earning hundreds of thousands of dollars for energy services once monopolised by large thermal plants. This income can then be reinvested into further decarbonisation or used to hedge against future volatility.
Breaking through the energy deadlock
Collectively, these AI-centred use-cases are helping to crack what the World Energy Council calls the enduring energy trilemma: how to supply secure electricity, meaning always available, affordable and equitable, priced so households and industries can thrive and environmentally sustainable, being aligned with net zero.
A 2024 report from the Council notes that with geopolitics upsetting gas markets and extreme weather hitting networks, more than half of the 127 countries it tracks slid backwards on at least one of these three pillars last year, and that resilience gaps are widening fastest in regions with rapid solar build-outs.
In solutions to address these issues, AI is a unifying thread. By sharpening forecasts for solar, pre-dispatching storage, and nudging demand to follow renewable peaks, algorithms bolster security without costly fossil back-ups. Because those optimisations squeeze extra kilowatt-hours out of every panel, they also dampen wholesale price spikes, advancing the affordability/equity pillar. And every watt delivered from optimised renewables displaces a fossil watt, directly lifting the sustainability score.
The benefits are enormous: the IEA’s Electricity Mid-Year Update projects that soaring deployments of solar PV will push the renewable share of global generation to ≈35 % in 2025, overtaking coal for the first time ever, if grids can integrate that variable output. That ‘if’ is the challenge AI is uniquely positioned to resolve.
Tomorrow will be powered by smarter energy
As climate challenges increase, infrastructure ages, and power demands surge, AI offers something rare: scalability, speed, and tangible results. It is already proving itself across the value chain, from stabilising renewables and boosting efficiency to enabling grid independence through intelligent microgrids.
But the existence of a technology will not ensure success alone. That now hinges on execution and urgency. Energy intensive industries, in particular, must move beyond pilots and weave AI into core operations. Regulators should accelerate digital-grid upgrades and actively promote data-sharing standards so that solar and storage assets ‘speak’ a common language.
Universities can help by opening weather and satellite data sets, further sharpening AI’s predictive power. Financial institutions, too, have a role to play: green banks and investors must fund the digital layer of energy transition with the same urgency as physical infrastructure.
The energy map is being redrawn in real time. Delivering on climate commitments, maintaining reliability and unlocking new value streams will come to organisations that embrace AI as a foundational enabler. Winning in clean energy is no longer just a question of capacity, but of intelligence.
