T. Räsänen

发表

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

J. Kukkonen, K. Karatzas, M. Kolehmainen, 2011, The Science of the total environment.

Digital age business model innovation for sustainability in University Industry Collaboration Model

A. Happonen, T. Räsänen, Ulla Santti, 2020, E3S Web of Conferences.

Designing a Business Model for Environmental Monitoring Services Using Fast MCDS Innovation Support Tools

A. Happonen, T. Räsänen, M. Kajanus, 2017 .

A SOM-BASED TOOL FOR PROCESS STATE MONITORING AND OPTIMIZATION

J. Huhtinen, T. Hiltunen, T. Räsänen, 2006 .

Analysing Contaminant Mixing and Dilution in River Waters Influenced by Mine Water Discharges

T. Räsänen, Kaisa Turunen, Emmy Hämäläinen, 2020, Water, Air, & Soil Pollution.

Neural Network based method for predicting regional visitor attendance levels in recreational areas

M. Kolehmainen, T. Räsänen, 2008 .

Monitoring groundwater quality with real-time data, stable water isotopes, and microbial community analysis: A comparison with conventional methods.

I. Miettinen, T. Pitkänen, Jenni Ikonen, 2022, The Science of the total environment.

Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data

Mikko Kolehmainen, Harri Niska, Kostas Karatzas, 2010 .

Feature-Based Clustering for Electricity Use Time Series Data

Mikko Kolehmainen, Teemu Räsänen, M. Kolehmainen, 2009, ICANNGA.

Reducing energy consumption by using self-organizing maps to create more personalized electricity use information

Mikko Kolehmainen, Teemu Räsänen, Juhani Ruuskanen, 2008 .

Intercomparison of air quality data using principal component analysis, and forecasting of PM₁₀ and PM₂.₅ concentrations using artificial neural networks, in Thessaloniki and Helsinki.

J. Kukkonen, K. Karatzas, M. Kolehmainen, 2011, The Science of the total environment.