Energy Optimization Platform for Buildings

About the Client

The client is a Norwegian services company providing smart solutions for energy systems. Its core focus is improving energy performance in buildings and industrial facilities while supporting more efficient use of global resources.In today’s market, where energy prices, sustainability targets, and operational efficiency are critical for property owners and industrial companies, the client’s solution addresses a highly relevant need: reducing energy waste without compromising indoor comfort.

Norway

Objective

The client aimed to develop an AI-based solution for optimizing energy consumption in buildings and factories. The business problem was clear: a significant share of generated electricity is consumed by the industrial sector, while a large portion of that energy is still wasted.

Solution

Altabel team started the development of the platform almost from scratch and now it's a full scale solution rich in functionality having a wide range of options to choose from.

List of functionality:

1) For ordinary end users:

A dashboard displaying parameters from sensors in real-time
Possibility to set temperature in the room/zone/building and working hours when this level of temperature should be maintained
Possibility to review buildings` historical dataSavings calculator

2) For admin personnel:

A dashboard with the map of all client buildings (grouped by organizations) and displaying parameters from sensors in real-time, that could be managed by the administrator
Possibility to add new sensors: admin can select the type of device, brand and connect it as a new source through login and password; then the robot analyzes all time series and gives the the operator the possibility to choose what time series should be used in particular cases
Creation of separately controlled zones in buildings with predefined set of sensors
Development of machines managing a predefined set of controlled devices
Possibility to create/choose algorithms ( for instance “Economical”)
User rights management functionality
Own API with endpoints so the customers are able to integrate our solution into their system with the help of API-keys
Collection of data from the European exchange on the electricity prices
Connection to system showing smart meter readings
Alerts management system to track algorithms condition, time series failure
Operating mode management system: the administrator has the possibility to deactivate some parts of the system (and, for example, switching devices into offline mode) in case of a system failure

Another part of the Altabel team has been working on the algorithms:

Currently the solution has 2 algorithms (engines) written in Python using ML and predictions that work on a separate server. The operation of algorithms is monitored, data is logged. The data is collected by the solution developed and transferred to the algorithms for analyses.

1. Ventilation Algorithm. The algorithm adjusts fan speed of ventilation units to expected CO2 concentration within the next 10 minutes. Future CO2 concentration is predicted using data from the last 24 hours.

2. Temperature regulation. The algorithm adjusts Supply Air Temperature and thermostat setpoint temperature. Typically a building loses heat in cool weather depending on outdoor temperature as well as wind strength and direction. When heating is turned on, temperature will typically increase up to the given setpoint. The time used to reach the setpoint will depend on the heating technology available in the building. The algorithm estimates the heat loss per hour when heating is switched off given the weather forecast for the coming night and predicts the reheat time.

Technologies

Ruby

React

Python

Result

Altabel helped the client move from an early-stage development need to a dedicated long-term development setup. The platform has grown from near-scratch development into a full-scale smart energy optimization solution with real-time dashboards, sensor integrations, building and zone management, API capabilities, energy price data collection, alerting, operating mode controls, and ML-based optimization engines.

The solution supports the client’s core goal: reducing energy consumption in buildings and factories while preserving thermal comfort during business hours.

Team 1 Data Scientist, 1 Backend Developer

AI Solution for Energy Consumption Optimization

About the Client

Objective

Solution

Result

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