#energydata

Adani Enterprises has committed $100 billion toward sovereign AI data centres by 2035.

Simultaneously, internal Grid-India memos show disputes over the location of Automated Meter Reading servers for the entire country.

A Layer 5 reading suggests this is not only about sovereignty, but about data enclosure.

If private infrastructure hosts the grid’s energy stack — meters, billing, dispatch logs — it controls the single source of truth for the national power economy.

Server custody translates into influence over commercial rules and operational records.

The scale of investment positions data centres as a foundational layer of future power system governance.

Energy demand is no longer a static metric — it’s dynamic, volatile, and deeply influenced by factors like weather patterns, grid constraints, geopolitical events, and changing consumption behaviours. Traditional approaches that rely on monthly or even weekly reporting can’t keep up with this pace. 

That’s why more utilities and energy providers are shifting to real-time demand intelligence. By continuously collecting and analysing data from IoT sensors, smart meters, weather APIs, and market signals, they can:  ⚡ Balance load dynamically and prevent outages  📈 Predict demand spikes before they happen  🌍 Improve grid sustainability and operational efficiency 

This isn’t just a technology upgrade — it’s a strategic advantage. Organisations that embed real-time intelligence into their operations make faster decisions, optimise resources, and stay ahead of market volatility. 

GoNetZeroTM introduces Renewable Energy Navigator Explorer (René), a conversational AI that uses natural language processing to enable streamlined energy insights. René helps leaders manage energy assets across enterprises by providing real-time, worldwide accessibility. With text searches, users may easily create energy data, enabling well-informed decision-making.

Companies are finding it difficult to achieve net-zero targets, according to a recent GoNetZeroTM survey conducted in Asia. The poll cited knowledge gaps in decarbonization strategies and difficulties getting budget approval for renewable energy projects.

René fills in these gaps by answering questions regarding energy use, predicted energy expenditures, prospective solar installation revenues, Renewable Energy Certificates (RECs), and revenue from energy sales. All of this is done through conversational text that is simple to understand. Investments in renewable energy are validated and optimum asset performance is strengthened by this data.

The CEO of GoNetZeroTM, Soon Sze Meng, highlights René's contribution to transforming energy data management, promoting educated debates, and assisting companies in achieving net-zero objectives. René is an essential tool for making well-informed decisions and scaling up initiatives toward sustainability.

A global decarbonization platform called GoNetZeroTM provides strategies for reaching net-zero objectives. GoNetZeroConnect is part of its portfolio of tools, which also includes access to verified renewable energy certificates and carbon credits, and helps with emissions measurement, abatement, offsetting, and reporting. Energy assets from several sources are scaled and managed by the platform's NetZeroOS. Sembcorp Industries, a prominent energy and urban solutions provider listed on the Singapore Exchange, is the parent company of GoNetZeroTM.

Artificial intelligence (AI) can realise the enormous potential of renewable energy fully.

 Fossil fuels will not be the primary energy source in the future. Renewable energy is establishing itself as the most reliable option for the future, but there are several daunting challenges despite significant progress in some areas. Solar and wind energy are primary renewable energy sources. People must handle the fluctuating weather and unstable flow of energy consumption. Storage advancement, while encouraging, is still far from where it should be. Some renewable energy generation cannot supply the baseload required for consumption. The baseload is no longer staying with renewables, making the transition pointless. During scarcity, reducing energy demand can potentially help. Lifting controls at the equipment and appliance level enable large air-conditioning units or industrial furnaces to use more energy when the supply is plentiful. In addition, battery packs and stored energy may be contracted by the owner of this equipment. The issue, however, is implementation. To set tariffs, a network must first determine the number of devices and their participation level. In addition, it must ensure that the energy consumption data from those devices is not misused or misinterpreted.

The majority of these issues can be resolved through AI and machine learning technologies. Machine learning is used to calculate appliance behaviour by applying advanced sensors, smart metres, and intelligent devices outside the metre. They can use algorithms to forecast storage life and determine the appropriate pay-outs.

Grid operators use AI to identify operational data such as solar panels and cooling systems for retail, commercial, industrial, and railway customers to improve real-time demand flexibility decisions. The real-time data collected from Germany's wind turbines and solar panels is used to forecast energy generation for the next two days.

Additionally, AI can facilitate demand flexibility by utilising game theory algorithms to create incentives that increase overall participation and leverage blockchain or other distributed ledger technologies to safeguard data. It is possible to create a marketplace in which consumers can participate in local demand-side management initiatives.

While managing renewable energy's intermittency is the primary objective, AI can also help the industry improve its safety, reliability, and efficiency. For example, predictive analytics can monitor the wear and tear on a wind turbine and predict when it will require maintenance with a high degree of accuracy. Additionally, it can provide visibility into energy leakage, consumption patterns, and the health of equipment.

As part of our work decomposing growth in greenhouse gas emissions into its key factors that was published in 2019 [1], we delved into historical data to create benchmarks against which trends in scenario projections could be compared. For our historical trends we relied on the long term data in the Primary, Final, and Useful (PFU) energy database, from IIASA. That data source goes back many decades, which makes it unique among such data sources

Before our article was published we examined comparable historical energy balance data from the International Energy Agency to see what it could tell us. The IEA global energy balances don’t go back as far as the PFU data, but are more detailed in some ways. This post describes the high level results from that review.

First, let’s look at an equation known as the Kaya Identity, which describes fossil carbon emissions as the product of four terms: Population, GDP/person (wealth), Primary Energy/GDP, and Carbon dioxide emissions/primary energy.

Over time, analysts have realized that this four-factor identity collapses some important information. That’s why, in our 2019 article, we moved to the expanded Kaya identity, with several more terms:

The components of this identity are as follows:

CFossil Fuels represents carbon dioxide (CO2) emissions from fossil fuels combusted in the energy sector,

P is population,

GWP is gross world product (measured consistently using Purchasing Power Parity here),

FE is final energy,

PE is total primary energy, calculated using the direct equivalent (DEq) method (electricity from non-combustion resources is measured in primary energy terms as the heat value of the electricity to first approximation), 

PEFF is primary energy associated with fossil fuels,

TFC is total fossil CO2 emitted by the primary energy resource mix,

NFC is net fossil CO2 emitted to the atmosphere after accounting for fossil sequestration.

For historical data, there is no sequestration of carbon dioxide emissions, so the last term is dropped in our graphs below.

Note that this identity applies only to carbon dioxide emissions from the energy sector. We use an additional additive dashboard for future scenarios to describe industrial process emissions, land use changes, and effects of other greenhouse gases, but we haven’t yet compiled those additional data for historical analysis and we only present the graphs for energy sector total fossil carbon dioxide emissions here.

The first graph is what we call our graph of key factors, from the indented list above. In the first row we show each term in its raw form. The second row shows indices with 1971 = 1.0. And the last term shows the annual rate of change in each term.

The total fossil carbon is the end result of the other factors, which drive emissions. It grows by about a factor of two from 1971 to 2016.

Download higher resolution version of Energy Sector Factors

The 2nd graph below shows the expanded Kaya identity ratios. Population is the same, but all the other columns show ratios from the 2nd equation above. Population and wealth per person (the first two terms in the Kaya identity) are the biggest drivers of emissions, while the energy intensity of economic activity declines to offset some of the growth in the first two terms. The other terms don’t show much change over the past 45 years.

Quantitatively, population and GWP per person both roughly double, while energy intensity of economic activity drops by half, with other factors roughly constant. That is consistent with Total Fossil Carbon increasing by a factor of two over this period.

Download higher resolution version of Energy Sector Ratios

These graphs are a handy summary of key historical data from IEA. If you want to see the longer term trends from IIASA’s PFU data, please email me and I’ll send you a copy of our 2019 article, which has those graphs. Happy to share the spreadsheets + graphs for those interested.

References

We recently completed sub-metering initiatives at the MTA’s Tuskegee Airman Bus Depot in Manhattan and at the SUNY Stony Brook University recreation center.

The initiatives are a harbinger of similar endeavors that we expect to undertake in the future, coupled with our NY Energy Manager. The goal: to provide customers with effective and easy-to-understand knowledge of energy consumption for individual equipment assets at their specific facilities for spearheading energy efficiency improvements and savings. 

LISTEN: Ingrid Akerlind, Fellow for Third Way's Clean Energy Program, spoke to NPR about how the internet and access to data about energy could help consumers and businesses save money and support renewable energy strategies.