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Smart Energy Management Smart solutions for the energy transition through data analysis with artificial intelligence

The Smart Energy Management team at the iHomeLab conducts research in the field of energy management in buildings with the aim of enabling intelligent analysis and control of energy production and consumption. This ensures a stable power supply, even with a high share of renewable energy sources. 

These efforts contribute significantly to a successful energy transition.

Visit the iHomeLab

Latest News

Media release: HSLU is developing data rooms for greater energy efficiency in buildings, making smart homes a reality for everyone.

The increasing number of electrical devices in households is producing more data, and HSLU is ready to meet the challenge. The SINA project is driving digitalisation forward and developing a cost-effective solution for the secure exchange of data between buildings and energy suppliers. Led by the Lucerne University of Applied Sciences and Arts with the participation of the iHomeLab, this initiative is set to revolutionise the way we save electricity. Read the HSLU media release from 16 October 2024 to learn more.

Smart Energy Analysis

To achieve this goal, the team uses advanced artificial intelligence methods to analyze energy consumption data in greater detail, in particular. This data analysis provides important insights into consumption patterns, which in turn can be used in the intelligent energy management of private apartments and houses. One example of this is optimizing self-consumption or increasing the level of stand-alone power.

In addition, the analyses at the energy distribution level help to avoid load peaks and thus minimize infrastructure expansion. Other application areas of energy data analysis include predictive maintenance and energy consumption forecasts.

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To simplify the use of energy management systems, the team relies on speech interfaces and researches Natural Language Processing (NLP) using machine learning.

The security of data transmission is crucial, which is why the team is researching secure Internet-of-Things architectures to guarantee data transmission.

In particular, the team's expertise includes:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Non-intrusive Load Monitoring (NILM)
  • Natural Language Processing (NLP)
  • Cybersecurity
  • Development of embedded systems and hardware.

The team has deep expertise in the areas of:

  • Smart Grid
  • smart meters
  • Load management
  • IoT technologies

Projects

For more than 15 years the iHomeLab has been successfully active in research and develops solutions in the field of Smart Energy Management in various projects.

WILSON

WILSON: Data-based solutions for sustainable operation and management of buildings and districts

Data modeling and digital twin technology for sustainable building and district management. With a focus on environmental, social and economic aspects, the project aims to support the achievement of EU energy and environmental targets by 2050.

SINA ECO Data Spaces

SINA creates a trustworthy data platform for efficient data exchange in the construction industry. Two sub-projects are defining basic principles and developing technology and rules. The aims are to reduce costs, promote digitalisation and long-term innovation.

Moost

MOOST Smart Home Activity Recognition

Activity recognition system for heterogeneous smart home setups. Create value for smarthome owners by providing them with highly personalized tips on how to better use their system.

DEMETER

DEMETER - Combining NLP and Trust

Automated collection and processing of agricultural data with "Natural Language Processing" and Machine Learning to make market information from the agricultural sector accessible to a broad mass with an app.

EVFlex Vehicle2Grid

EVFlex

Balancing of peak loads in the grid with power of Batteries from Electric Vehicles (called Vehicle2Grid or V2G).

Giases Glättung von Lastspitzen

Giases - Smoothing of Peak Loads

Holistic Integrating Adaptive Smart Energy System (GIASES) for smoothing peak loads with flexible consumers and optimizing the production planning of power plant operators.

Projekt FAST

FAST

Fitness device with adaptive heart rate control for older people based on an e-bike

Forschungsprojekt Bodyguard

Bodyguard

The use of voice control in smart homes offers a high level of comfort. But the microphones are always on, always connected - and sensitive data can inadvertently get out onto the Internet. We are developing a device that works with voice control and does not need the Internet at all.

Distinguishing Performance Indicators

DPI - Distinguishing Performance Indicators

Development of intelligent algorithms for energy management platform for a building complex.

intelligente Waschmaschine

NILM4Balance

In NILM4Bal we investigate how smart meter data can be used to automatically identify large power consumers and quantify their load shift potential.

Forschungsprojekt non intrusive Load Monitoring

NILMplus

In this project, we are investigating a solution for industrial load disaggregation that combines the cost efficiency of a NILM system with the accuracy of classical submetering.

Forschungsprojekt enerFACEpredict

enerFACEpredict

Timing is everything. Especially owners of solar plants are aware of that fact. Because the more precisely they can regulate their power consumption, the less they need to purchase external power. Calculating the optimal timing is a complex matter. We try to find a way in our new research project.

Projekt Smart Maintenance

Smart Maintenance 4.0

"Predictive Maintenance": Can sensor data be used to predict when a machine will need maintenance?

Forschungsprojekt non intrusive Load Monitoring

NILM@I

The aim of the project is to obtain a breakdown of individual consumers in the industrial sector on the basis of a central measurement of energy consumption.

Scientific Papers on Smart Energy Management

Partners

The application-oriented and interdisciplinary research projects are carried out in close cooperation with industrial partners and end-user organisations. The research partners automatically become part of our network, which currently comprises over 200 partners. They meet at network events and have the opportunity to establish contacts for future projects.


The iHomeLab can look back on a successful and long-standing cooperation with its partners.