

4 “Reasons” Why Not to Use AI in Energy Sector (And Why They Don’t Hold Up)❌
AI has become a buzzword in the energy sector, promising efficiency, cost savings, and data-driven decision-making. Many see its potential and are intrigued by what
Unlike traditional methods that use smart meter data only for compliance and reporting, we unlock its full potential to deliver meaningful savings and performance gains.
Jan Široký
CEO
Our AI tools analyze smart meter data to detect anomalies, uncover inefficiencies, and provide a clear overview of energy use. Automated and scalable, they enable fast, data-driven decisions.
Energy Twin energy experts
continuously monitor results
and collaborate with
managers and technicians
to interpret data and resolve
issues quickly and
effectively.
Our team goes beyond
monitoring by proactively
analyzing the results and
proposing tailored solutions to
specific challenges, ensuring
we achieve the best possible
outcomes.
Client managed 53 buildings but lacked a data-driven approach to prioritize energy savings.
We analyzed their portfolio using CSV meter data, uncovering operational issues and pinpointing buildings with the highest savings potential.
Client needed to prepare quarterly budgets for 40+ branches without manual data handling.
We automated data processing and provided scalable estimates, streamlining the budgeting process
Client wanted to confirm if a newlybuilt/reconstructed building was operating efficiently
We generated a report within minutes, identifying potential energy savings and validating performance.
Client was managing 80 buildings and needed to detect operational inefficiencies in real-time, beyond traditional billing analysis.
We developed anomaly detection rules using our models, enabling real-time monitoring of inefficiencies and immediate action.
Client implemented Energy Conservation Measures (ECMs) and needed to quantify weather-normalised savings.
We used baseline models to compare measured data with predictions, quantifying savings and identifying buildings where ECMs underperformed.
Client managed 60 buildings of similar purpose and wanted a way to compare their performance
By integrating additional data like m², we created KPIs to benchmark performance across buildings, identifying anomalies like excessive setback consumption while factoring in weather conditions.
Data Analysis (2 months)
Optimization and Monitoring
AI has become a buzzword in the energy sector, promising efficiency, cost savings, and data-driven decision-making. Many see its potential and are intrigued by what
It’s safe to say we’re not exactly huge fans of banks… as customers. Thankfully, digitalization has rescued most of us from the tedious in-person trips
Every journey into AI begins with a crucial first step: understanding your data. Many clients approach us eager to jump straight into machine learning (ML),
This tutorial is focused on visualization of a model in the app ET Views once it has been identified.
This tutorial is focused on visualization of a model in the app ET Views once it has been identified.
This tutorial is focused on visualization of a model in the app ET Views once it has been identified.