Analyzing the Energy Market in the Era of Artificial Intelligence
I recently discovered a fascinating company called REint, which is at the forefront of the energy transition. They are actively developing AI-powered technologies aimed at advancing renewable energy adoption, enhancing grid resilience, and expediting the realization of a net-zero energy grid.
I spent some time to go about doing some market analysis thought it is a fun exercise
The analysis is an attempt to showcase how I go about market analysis (sizing, ICPs and potential wedge and expansion) for early stage companies.
Some background on REint - They leverage the power of artificial intelligence to predict and forecast renewable energy resources, specifically focusing on solar and wind energy, while also factoring in short-term weather patterns.
I referred only to the company’s website so it is an external perspective.
From the website, I could gather that REint serves the following ICPs -
1. Energy Traders
2. Utilities
3. Smart Home Management Systems
Each of these ICPs has its own nuance and will require different product wedges to be able to work well & solve it.
From my research, the energy traders market seemed like something that is already mature and can adopt a solution like this faster with relatively shorter sales cycles.
Let us analyze this further.
Energy traders
I see this business as an equivalent of Bloomberg Terminal. The reason for the same is that I think REint can resemble a data platform at its eventual end state.
Another example of such a business is the World Bank. They are a data platform for companies to ingest data for general commodities. I have the experience of working with this at my current company (a fortune 200 company).
Analyzing the same for Bloomberg, this is how the TAM potentially looks like this -
Bloomberg sells to financial institutions and has an ACV of $25,000 and worldwide it has 325,000 subscribers which stands at $8.05B+ in annual revenue.
The emphasis is laid on energy sectors at 0.4% in JP Morgan & 0.6% in Goldman Sachs on an average considering this 0.5% is the percentage of subscribers in each of the companies (Linkedin).
The % of employees in Goldman Sachs and JP Morgan investing in the energy sector is 0.6% and 0.4% respectively. Hence, taking a cumulative average of 0.5% of total investment professionals in the world making energy investments helps me arrive at a number of potential users from the install base of Bloomberg
So taking into account the worldwide subscribers of Bloomberg ~ 1625.
Subscribers are for the energy sector and the price of each subscription is $25,000.Bloomberg has 1/3rd market share so the subscribers could be 3x and could stand at ~ 4,875.
📌 Hence, the net TAM for the market selling to Energy Traders is ~$122M.
Utilities
There are many utilities around the world that are incorporating renewable resources into their energy mix. Some examples include:
Enel is an Italian utility that has committed to producing 100% of its electricity from renewable sources by 2050.
NextEra Energy is a US-based utility that is one of the largest producers of wind and solar power in the world.
E.ON is a German utility that has set a target of producing 100% of its electricity from renewable sources by 2025.
Ørsted is a Danish utility that has transformed itself from a fossil fuel-based company to a world leader in offshore wind power.
Many utilities are using Energy Management Systems (EMS) to optimize their energy production and distribution processes. EMS are software platforms that allow utilities to monitor and control their energy assets in real time, enabling them to make data-driven decisions that improve efficiency and reduce costs.
The EMS market can be divided into the following →
Measurement devices
Communication network
Software applications
All of these are possible directions that the founders can take but my sense is that each of them will require its own set of nuances.
Given that short-term load forecasting is indicated on the website, it is a natural fit for the founders to incorporate their capabilities into the software application layer of the energy management system, as load forecasting assists in a variety of ways.
To effectively integrate multiple energy resources, such as rooftop solar, photovoltaic cells, electric vehicles, and demand response, into the distribution system, it is essential to have accurate load forecasting analytics.
These analytics help in determining the time, quantity, and location of energy resources' contributions to the power grid while balancing traditional generating resources to satisfy demand.
One thing to note here is companies there are two kinds of companies in this category-
Etap - Companies that create an end - to - end energy management solutions which has load analytics/predictive layer as one of their components
Hivepower, sensewaves, Amperon - Companies that create an intelligence layer/SaaS on existing hardware components that are IoT bases.
Some use cases are-
Predictive tools - Energy forecasts
Short-term forecasts for accurate scheduling and position management
Fully-automated AI scheduling based on demand forecasts
Optimise electric vehicles' charging cycles
Analysis of existing, sparse grid information, e.g. smart meter data from
houses/buildings, to provide Distribution System Operators (DSOs) end-to-end visibility into the energy flows and congestion levels at any point/asset in the grid.
In my opinion, REint fits best in the latter category (companies that create an intelligence SaaS layer).
My perspective here is that REint plays in the Software Applications category of EMS. Here, there is a class of companies that create an intelligence SaaS layer on top of existing hardware components like Amperon and Sensewaves. My sense is that REint fits best in this category.
Global market for SaaS-based energy technologies is projected to grow from $3.5 billion in 2021 to $17.5 billion in 2030, at a CAGR of 19.7%.
Smart Home Management System
The market drivers for the home energy management category are →
Rising demand for an energy-efficient solution
Rising urbanisation in developing economies
Increasing number of connected devices through the Internet of Things.
Different Government initiative toward the construction of smart home is expected to create lucrative opportunities for the market. Most smart home management systems could witness more growth given the following features are being given more importance to →
Electricity consumption monitoring
Efficient use of solar energy
Backup management using battery storage
Consumption Monitoring is a space in the Smart Home Management System that I believe can be REint’s wedge to enter the market of home energy management system market. These systems need to control and monitor various devices and appliances at home and interactions happen in →
Device information can be provided to the user through a web interface or a phone app/tablet.
For better device monitoring and control, HEMS (home energy management system) should provide seamless communication between different smart devices and sensors via ZigBee, Z-Wave, Wi-Fi, etc.
ZigBee is a wireless, low-power mesh networking standard that is widely used in wireless control and monitoring applications, while its low power consumption allows use with smaller batteries.
Potential ideas that REint can explore in this category as a SaaS product →
SaaS can be used to provide real-time monitoring and control of energy usage in smart home devices. This can help users to identify devices that consume a lot of energy and take steps to reduce their usage.
To tackle the issues of high energy costs, increasing demand for power grids and to assist the rising number of electric vehicle drivers, a new home energy monitoring and management solution needs to be available through its single smart home app.
SaaS can be used to provide analytics capabilities that can help to identify patterns in energy usage. For example, analytics can be used to identify times of day when energy consumption is highest and suggest ways to reduce energy usage during those times.
SaaS can be used to provide machine learning capabilities that can help to optimize the performance of smart home devices. For example, machine learning algorithms can be used to learn user preferences and automatically adjust device settings to reduce energy consumption.
SaaS can be used to provide remote access and control of smart home devices, which can help users to monitor and control their devices from anywhere in the world. This can help users to turn off devices when they are not in use, reducing energy consumption and saving money on utility bills.
📌 Smart home energy management device market size is projected to grow from $1.1B in 2023 to $4.1B in 2032
📌 Total TAM = TAM for Smart Home Energy Management + Utilities + Energy Traders, which comes down to $4.7B
Questions for us to think more about
They’re a forecasting platform but the main question is if they can focus on a smaller area/region - for a city or a block and if can they forecast the capacity and the need for electricity in the region. This is not clear to me from their website.
The market segment for energy traders could involve a huge services component if that resembles the nature of Bloomberg’s business. How is REint thinking about the same?
How is REint planning to enter the utilities segment given this is a broad and hardware-heavy industry?
EV- The only use case I was able to come across was monitoring the charging cycles. The way I look at EVs is clean energy more than renewable energy. More use cases are to be discussed.
Each of the directions discussed above requires different product roadmaps and GTM motions. Is there a prioritization that the REint team is thinking about?
I conducted an external analysis of the industry, and I am happy to learn from builders and folks who have worked on this space. Your insights and input are welcomed and valued.
Until then, you’ve been thunderstruck!