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Harri Niska

Harri Niska

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Elinkeinoelämän ja yliopiston välinen yhteistyö
–Yrityskumppanuudet
–Ekosysteemi- ja sidosryhmäyhteistyö
–Koulutusyhteistyö (mm. FITech)

Kehittämispalvelut, Yliopistopalvelut

harri.niska@uef.fi | 044 265 1291

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Tiedot Julkaisut

Projektit

UpTech, tulevaisuuden osaajia Itä-Suomeen Päättynyt 01.07.2020 - 30.06.2023

Avainsanat

datatiede energiajärjestelmät koneoppiminen koulutuksen kehittäminen tekoäly tiedonlouhinta yliopisto-yritys yhteistyö ympäristöinformatiikka ympäristötekniikka yrityskumppanuus

Julkaisut

53 kappaletta

A physical-neural network approach for residential load forecasting with dynamic load control
Nakabi, T; Brester, C; Kolehmainen, M; Niska, H. Teoksessa: (toim.) , 2023. 27th International Conference on Electricity Distribution (CIRED 2023). s. 1-5. Institution of engineering and technology
Automated load control detection using power quality data and machine learning
Brester, C; Hildén, A; Kolehmainen, M; Pakonen, P; Niska, H. Teoksessa: (toim.) , 2023. 27th International Conference on Electricity Distribution (CIRED 2023). s. 1-5. Institution of engineering and technology
Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations
Brester, Christina; Kallio-Myers, Viivi; Lindfors, Anders V.; Kolehmainen, Mikko; Niska, Harri. 2023. Renewable energy. 207: 266-274
Aggregated forecasting of the load control responses using a hybrid model that combines a physically based model with machine learning
Koponen, Pekka; Salmi, Tuukka; Evens, Corentin; Takala, Suvi; Hyttinen, Antti; Brester, Christina; Kolehmainen, Mikko; Niska, Harri. Teoksessa: (toim.) , 2021. . s. 588-901.
Weather-Driven Predictive Control of a Battery Storage for Improved Microgrid Resilience
Gutierrez-Rojas, Daniel; Mashlakov, Aleksei; Brester, Christina; Niska, Harri; Kolehmainen, Mikko; Narayanan, Arun; Honkapuro, Samuli; Nardelli, Pedro HJ. 2021. IEEE access. 9: 163108-163121
Assessing and Comparing Short Term Load Forecasting Performance
Koponen, Pekka; Ikäheimo, Jussi; Koskela, Juha; Brester, Christina; Niska, Harri. 2020. Energies. 13: 2054
Weather-based fault prediction in electricity networks with artificial neural networks
Brester, Christina; Niska, Harri; Ciszek, Robert ; Kolehmainen, Mikko. Teoksessa: -(toim.) , 2020. 2020 IEEE Congress on Evolutionary Computation (CEC): Conference proceedings. s. 1-8. IEEE
Combining the strengths of different load modeling methods in short-term load forecasting of a distribution grid area with active demand
Koponen, Pekka; Niska, Harri; Mutanen, Antti. Teoksessa: -(toim.) , 2019. CIGRE SC D2 Colloquium 2019 - Helsinki, Finland, 11- 14 Jun 2019. s. 1-8. Conseil international des grands réseaux électriques
Mitigating the Weaknesses of Machine Learning in Short-Term Forecasting of Aggregated Power System Active Loads
Koponen, Pekka; Niska, Harri; Mutanen, Antti. Teoksessa: -(toim.) , 2019. Proceedings: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN). s. 303-310. IEEE
Data analytics approach to create waste generation profiles for waste management and collection
Niska, Harri; Serkkola, Ari. 2018. Waste management. 77: 477-485
Effects of Electric Vehicles and Heat Pumps on Long-Term Electricity Consumption Scenarios for Rural Areas in the Nordic Environment
Haakana, Juha; Haapaniemi, Jouni; Lassila, Jukka; Partanen, Jarmo; Niska, Harri; Rautiainen, Antti. Teoksessa: -(toim.) , 2018. 15th International Conference on the European Energy Market (EEM). s. 1-5.
Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response
Koponen, Pekka; Hanninen, Seppo; Mutanen, Antti; Koskela, Juha; Rautiainen, Antti; Jarventausta, Pertti; Niska, Harri; Kolehmainen, Mikko; Koivisto, Hannu. Teoksessa: -(toim.) , 2018. 2018 IEEE International Energy conference (ENERGYCON), Limassol, Cyprus, 3-7 June 2018. s. 354-359. IEEE
Hybrid model for short-term forecasting of loads and load control responses
Koponen Pekka, Niska Harri. Teoksessa: -(toim.) , 2017. 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). s. 1-6.
Forest Industry Hygiene Control with Reference to Waste Refinement
Hakalehto Elias, Heitto Anneli, Niska Harri, Suhonen Anssi, Laatikainen Reino, Heitto Lauri, Antikainen Eero, Jääskeläinen Ari. Teoksessa: Eino Elias Hakalehto(toim.) , 2016. Microbiological Industrial Hygiene. s. 151-185. Nova science publishers
Identifying socio-demographically favourable area types for early hybrid electric vehicle adoption through mining of public sector data
Saarenpää Jukka, Kolehmainen Mikko, Niska Harri. 2016. Intelligent data analysis. 20: 339-355
Mining smart meter data -case Finland
Mutanen Antti, Järventausta Pertti, Niska Harri. Teoksessa: -(toim.) , 2016. IET Conference Publications.CIRED 2016 Workshop 14-15 June 2016. s. 120. Cired
Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table
Huttunen Jani, Kokkola Harri, Mielonen Tero, Mononen Mika Esa Juhani, Lipponen Antti, Reunanen Juha, Lindfors Anders Vilhelm, Mikkonen Santtu, Lehtinen Kari Erkki Juhani, Kouremeti Natalia, Bais Alkiviadis, Niska Harri, Arola Antti. 2016. Atmospheric chemistry and physics. 16: 8181-8191
A data mining approach for producing small area statistics-based load profiles for distribution network planning
Saarenpää Jukka, Kolehmainen Mikko, Mononen Matti, Niska Harri. Teoksessa: IEEE(toim.) , 2015. IEEE ICIT 2015 Proceedings. s. 1236-1240. IEEE
Evolving smart meter data driven model for short-term forecasting of electric loads
Niska Harri, Koponen, Pekka, Mutanen, Antti. Teoksessa: Palaniswami, Marimuthu Swami, Tan, Hwee Pink(toim.) , 2015. IEEE ISSNIP 2015 Proceedings. s. 1-6.
Monetary impact of dynamic pricing and demand response on households: the winners and losers
Mononen Matti, Rautiainen Antti, Saarenpää Jukka, Kolehmainen Mikko, Niska Harri. Teoksessa: 2015 IEEE Power & Energy Society(toim.) , 2015. IEEE Conference Publications. s. 1-5. IEEE
Optimized Curve Design For Image Analysis Using Localized Geodesic Distance Transformations
Braithwaite Billy, Niska Harri, Pöllänen Irene, Ikonen Tiia, Haataja Keijo, Toivanen Pekka J., Tolonen Teemu. 2015. Proceedings of SPIE : the International Society for Optical Engineering
Assessment of Some Methods for Short-Term Load Forecasting
Koponen Pekka, Mutanen Antti, Niska Harri. Teoksessa: -(toim.) , 2014. Proceedings of IEEE ISGT Europe 2014. s. 6 s. Institute of Electrical and Electronics Engineers IEEE
Computer-aided breast cancer histopathological diagnosis: Comparative analysis of three DTOCS-based features: SW-DTOCS, SW-WDTOCS and SW-3-4-DTOCS
Pöllänen Irene, Braithwaite Billy, Ikonen Tiia, Niska Harri, Haataja Keijo, Toivanen Pekka, Tolonen Teemu. Teoksessa: -(toim.) , 2014. Proceedings of 4th International Conference on Image Processing Theory, Tools, and Applications (IPTA2014). s. 1-6. IEEE
Computer-assisted image analysis of histopathological breast cancer images using step-DTOCS
Ikonen Tiia, Niska Harri, Braithwaite Billy, Pöllänen Irene, Haataja Keijo, Toivanen Pekka, Tolonen Teemu, Isola Jorma. Teoksessa: -(toim.) , 2014. Proceedings of the 14th International Conference on Hybrid Intelligent Systems (HIS2014. s. 187-192.
Data-driven method for providing feedback to households on electricity consumption
Mononen Matti, Saarenpää Jukka, Johansson Markus, Niska Harri. Teoksessa: -(toim.) , 2014. Proceedings of International Conference on Intelligent Sensing and Information Processing. s. -. IEEE
A regional model for sustainable biogas electricity production: A case study from a Finnish province
Huopana Tuomas, Song Han, Kolehmainen Mikko, Niska Harri. 2013. Applied energy. 102: 676-686
Computational approach for spatiotemporal modelling of heating loads using AMR and other external data
Niska Harri; Saarenpää Jukka; Kolehmainen Mikko. Teoksessa: Pilo, F; Lama, R; Caro Moron, F. J.(toim.) , 2013. CIRED 2013: 22nd International Conference and Exhibition on Electricity Distribution. s. 23/27. IET Services Ltd
Extracting controllable heating loads from aggregated smart meter data using clustering and predictive modelling
Niska Harri. Teoksessa: M. Palaniswami, C. Leckie, S. Kanhere, J. Gubbi(toim.) , 2013. IEEE Eight International Conference on Intelligent Sensors, Sensor Networks and Information Processing. s. 368-373.
A REGIONAL MODEL FOR SUSTAINABLE BIOGAS PRODUCTION, Case study: North Savo, Finland
Tuomas Huopana, Harri Niska, Ari Jääskeläinen, Jaan Lõõnik, Emilia den Boer, Han Song, Eva Thorin. 2012. . .
Data-driven model for forecasting energy demand using continuous energy metering and other sensor data
Niska H, Johansson M, Raatikainen M, Skön J-P, Saarenpää J, Kolehmainen M. 2012.
GUIDELINE ON ACTION PLANS AND STRATEGIES TO MOBILIZE WASTE-TO-ENERGY PRODUCTION
Jaan Lõõnik, Reljo Saarepera, Maarja Käger, Thea Sogenbits, Priit Freienthal, Eva Thorin, Han Song, Bozena Guziana, Ari Jääskeläinen, Tuomo Eskelinen, Miika Kajanus, Tuomas Huopana, Harri Niska, Emilia den Boer, Jan den Boer, Ryszard Szpadt, Anna Behrendt, Dejan Vasilic, Thorsten Ahrens, Olga Anne, Arũnas Balčiũnas. 2012. . .
Predictive data-driven modeling approaches in environmental management decision-making
Niska Harri. 2012. Publications of the University of Eastern Finland. Dissertations in Forestry and Natural Sciences
DDM/CI methods and experiments in load modelling using AMR and other environmental data
Niska Harri, Saarenpää Jukka. 2011. VTT Working Papers. . Valtion taloudellinen tutkimuskeskus
Regional Heat and Power Production from Biogas in the Most Potential Heat Production Areas
Huopana Tuomas, Niska Harri, Kolehmainen Mikko. 2011. Nordic Bioenergy 2011 - Book of Proceedings
Scenario based Electricity Load Prediction Tool for Distribution Planning and Management
Niska Harri, Saarenpää Jukka, Räsänen Teemu, Tiirikainen Jarkko, Kolehmainen Mikko. 2011.
Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data
Räsänen T, Voukantsis D, Niska H, Karatzas K & Kolehmainen M. 2010. Applied energy. 87: 3538-3545
Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece
Voukantsis D, Niska H, Karatzas K, Riga M, Damialis A & Vokou D. 2010. Atmospheric environment. 44: 5101-5111
Neural networks for the prediction of species-specific stem volumes using airborne laser scanning and aerial photographs
Niska H, Packalén P, Skön J-P, Tokola T, Maltamo M & Kolehmainen M. 2010. IEEE transactions on geoscience and remote sensing. 48: 1076-1085
Use of sewer on-line total solids data in wastewater treatment plant modelling
Poutiainen H, Niska H, Heinonen-Tanski H & Kolehmainen M. 2010. Water science and technology. 62: 743-750
An experimental evaluation of ZCS-DM for the prediction of urban air quality
Tzima FA, Niska H, Kolehmainen M, Karatzas KD, Mitkas PA. Teoksessa: Athanasiadis IN, Mitkas PA, Rizzoli AE, Marx-Gómez J(toim.) , 2009. Information Technologies in Environmental Engineering. Proceedings of the 4th International ICSC Symposium. s. 291-304.
Neural networks for estimating net ecosystem CO2 exchange using incomplete eddy covariance data
Niska H, Shurpali N, Martikainen P, Kolehmainen M. Teoksessa: Fonseca I Casas, P(toim.) , 2009. Proceedings of the 18th IASTED International Conference on Applied Simulation and Modelling. s. 112-117.
Predictive system for monitoring regional visitor attendance levels in large recreational areas
Räsänen T, Niska H, Hiltunen T, Tiirikainen J, Kolehmainen M. 2009. Journal of environmental informatics. 13: 45-55
Characterisation of the chemical and biological properties of molecules with QSAR/QSPR and chemical grouping, and its application to a group of alkyl ethers
Niska H, Tuppurainen K, Skön J-P, Mallett AK, Kolehmainen M. 2008. Sar and qsar in environmental research. 19: 263-284
Explaining atmospheric aerosol nucleation events using data mining tools, back-trajectories and land cover characteristics
Junninen H, Hyvönen S, Niska H, Laakso L, Dal Maso M, Grönholm T, Bonn B, Keronen P, Aalto P, Hiltunen V, Pohja T, Launiainen S, Hari P, Mannila H, Kulmala M. Teoksessa: (toim.) , 2006. 7th International Aerosol Conference 2006. Abstract Book. s. . AAAR
Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series
Niska H, Heikkinen M, Kolehmainen M. Teoksessa: (toim.) , 2006. Intelligent Data Engineering and Automated Learning. IDEAL 2006. 7th International Conference, Proceedings. s. 224-231. Springer
Improved route planning and scheduling of waste collection and transport
Nuortio T, Kytöjoki J, Niska H, Bräysy O. 2006. Expert systems with applications. 30: 223-232
Evaluation of an integrated modeling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations
Niska H, Rantamäki M, Hiltunen T, Karppinen A, Kukkonen J, Ruuskanen J, Kolehmainen M. 2005. Atmospheric environment. 39: 6524-6536
Evolutionary design and evaluation of modeling system for forecasting urban airborne maximum pollutant concentrations
Niska H, Hiltunen T, Karppinen A, Kolehmainen M. Teoksessa: Ribeiro B, Albrecht RF, Dobnikar A, Pearson DW, Steele NC(toim.) , 2005. Adaptive and Natural Computing Algorithms. Proceedings of the International Conference. s. 181-184. Springer
Evolving the neural network model for forecasting air pollution time series
Niska H, Hiltunen T, Karppinen A, Ruuskanen J, Kolehmainen M. 2004. Engineering applications of artificial intelligence. 17: 159-167
Methods for imputation of missing values in air quality data sets
Junninen H, Niska H, Tuppurainen K, Ruuskanen J, Kolehmainen M. 2004. Atmospheric environment. 38: 2895-2907
Evolving the neural network model for forecasting air pollution time series
Niska H, Kolehmainen M, Hiltunen T, Karppinen A, Ruuskanen J. Teoksessa: A.E. Ruano(toim.) , 2003. IFAC International Conference on Intelligent Control Systems and signal Processing (ICONS'03). s. 310-315. Elsevier
Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki
Kukkonen J, Partanen L, Karppinen A, Ruuskanen J, Junninen H, Kolehmainen M, Niska H, Dorling S, Chatterton T, Foxall R, Cawley G. 2003. Atmospheric environment. 37: 4539-4550
Hybrid models for forecasting air pollution episodes
Niska H, Hiltunen T, Kolehmainen M, Ruuskanen J. Teoksessa: David W. Pearson, Nigel C. Steele, Rudolf F. Albrech(toim.) , 2003. Artificial Neural Nets and Cenetic Algorithms Proceedings of the International Conference. s. 80-84. Springer-Verlag

University of Eastern Finland Joensuu, Kuopio © University of Eastern Finland

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Näytä asetukset

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Tarkenna hakuasi

Harri Niska

Projektit

Avainsanat

A physical-neural network approach for residential load forecasting with dynamic load control

Automated load control detection using power quality data and machine learning

Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations

Aggregated forecasting of the load control responses using a hybrid model that combines a physically based model with machine learning

Weather-Driven Predictive Control of a Battery Storage for Improved Microgrid Resilience

Assessing and Comparing Short Term Load Forecasting Performance

Weather-based fault prediction in electricity networks with artificial neural networks

Combining the strengths of different load modeling methods in short-term load forecasting of a distribution grid area with active demand

Mitigating the Weaknesses of Machine Learning in Short-Term Forecasting of Aggregated Power System Active Loads

Data analytics approach to create waste generation profiles for waste management and collection

Effects of Electric Vehicles and Heat Pumps on Long-Term Electricity Consumption Scenarios for Rural Areas in the Nordic Environment

Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response

Hybrid model for short-term forecasting of loads and load control responses

Forest Industry Hygiene Control with Reference to Waste Refinement

Identifying socio-demographically favourable area types for early hybrid electric vehicle adoption through mining of public sector data

Mining smart meter data -case Finland

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

A data mining approach for producing small area statistics-based load profiles for distribution network planning

Evolving smart meter data driven model for short-term forecasting of electric loads

Monetary impact of dynamic pricing and demand response on households: the winners and losers

Optimized Curve Design For Image Analysis Using Localized Geodesic Distance Transformations

Assessment of Some Methods for Short-Term Load Forecasting

Computer-aided breast cancer histopathological diagnosis: Comparative analysis of three DTOCS-based features: SW-DTOCS, SW-WDTOCS and SW-3-4-DTOCS

Computer-assisted image analysis of histopathological breast cancer images using step-DTOCS

Data-driven method for providing feedback to households on electricity consumption

A regional model for sustainable biogas electricity production: A case study from a Finnish province

Computational approach for spatiotemporal modelling of heating loads using AMR and other external data

Extracting controllable heating loads from aggregated smart meter data using clustering and predictive modelling

A REGIONAL MODEL FOR SUSTAINABLE BIOGAS PRODUCTION, Case study: North Savo, Finland

Data-driven model for forecasting energy demand using continuous energy metering and other sensor data

GUIDELINE ON ACTION PLANS AND STRATEGIES TO MOBILIZE WASTE-TO-ENERGY PRODUCTION

Predictive data-driven modeling approaches in environmental management decision-making

DDM/CI methods and experiments in load modelling using AMR and other environmental data

Regional Heat and Power Production from Biogas in the Most Potential Heat Production Areas

Scenario based Electricity Load Prediction Tool for Distribution Planning and Management

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

Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece

Neural networks for the prediction of species-specific stem volumes using airborne laser scanning and aerial photographs

Use of sewer on-line total solids data in wastewater treatment plant modelling

An experimental evaluation of ZCS-DM for the prediction of urban air quality

Neural networks for estimating net ecosystem CO2 exchange using incomplete eddy covariance data

Predictive system for monitoring regional visitor attendance levels in large recreational areas

Characterisation of the chemical and biological properties of molecules with QSAR/QSPR and chemical grouping, and its application to a group of alkyl ethers

Explaining atmospheric aerosol nucleation events using data mining tools, back-trajectories and land cover characteristics

Genetic algorithms and sensitivity analysis applied to select inputs of a multi-layer perceptron for the prediction of air pollutant time-series

Improved route planning and scheduling of waste collection and transport

Evaluation of an integrated modeling system containing a multi-layer perceptron model and the numerical weather prediction model HIRLAM for the forecasting of urban airborne pollutant concentrations

Evolutionary design and evaluation of modeling system for forecasting urban airborne maximum pollutant concentrations

Evolving the neural network model for forecasting air pollution time series

Methods for imputation of missing values in air quality data sets

Evolving the neural network model for forecasting air pollution time series

Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki

Hybrid models for forecasting air pollution episodes