Harri Niska

University of Eastern Finland | UEF · Department of Environmental Science

Accurate prediction of energy consumption in district heating systems plays an important role in supporting effective and clean energy production and distribution in dense urban areas. Predictive models are needed for flexible and cost-effective operation of energy production and usage, e.g., using peak shaving or load shifting to compensate for he...

This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The pre...

When identifying and comparing forecasting models, there may be a risk that poorly selected criteria could lead to wrong conclusions. Thus, it is important to know how sensitive the results are to the selection of criteria. This contribution aims to study the sensitivity of the identification and comparison results to the choice of criteria. It com...

Machine learning methods predict accurately in situations that are adequately included in the learning data and do not require detailed domain knowledge based model development. They have their weaknesses compared with other forecasting methods, however. For example, they may fail in many new situations not experienced before. Hybrid models are inc...

Power flows are becoming increasingly volatile in power distribution grids. Distributed power generation, electricity storage, electrical vehicles and active demand cause these variations. Dynamic management of constraints, power quality and balancing will be needed. The accurate forecasting of the power flows is a necessary enabler for it. It is i...

Extensive monitoring data on waste generation is increasingly collected in order to implement cost-efficient and sustainable waste management operations. In addition, geospatial data from different registries of the society are opening for free usage. Novel data analytics approaches can be built on the top of the data to produce more detailed, and...

Abstract—A hybrid model for short-term forecasting of aggregated thermal loads and their load control responses is studied in this paper using field test data. Inputs include temperature measurement and forecast, measured power and control signals. The hybrid model comprises 1) partly physically based forecasting of the responses of the controlled...

In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve...

Smart meters collect a lot of data on customer level electricity consumption and this, together with other data sources e.g. environmental information and public open data, provides an excellent basis for data mining. As a part of a recent smart grid project conducted in Finland, several different ways of mining smart meter data were studied. The p...

In the coming years, the share of hybrid electric vehicles is expected to grow significantly in personal transportation. Vehicles that can be charged from the electrical grid, such as plug-in hybrids, could introduce problems for the distribution network, especially if the vehicle adoption is spatially concentrated and the charging happens unmanage...

In order to have a good estimate of the current forcing by anthropogenic aerosols knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from 1990’s onward. One option to lengthen the AOD time series beyond 1990’s is to retrieve AOD fro...

In Nordic countries, the forest exploitation has been for centuries based on the “Everyman’s Rights." This is an unwritten knowledge collection on acceptable behavior in the forests and in their utilization. Regardless of the ownership or possession of the land, every citizen has the right to collect berries mushrooms, camp and hike. However, neith...

Short-term forecasting of electric loads is an essential function required by Smart Grids. Today increasing amount of smart metering data is available enabling the development of enhanced data-driven models for short-term load forecasting. Until now, a plethora of models have been developed ranging from simple linear regression models to more advan...

The recent European Union and national level initiatives such as INSPIRE and PSI have increased the availability of public sector data, which provides interesting new opportunities to support decision making in electricity distribution network planning. With big amounts of available data, data mining methods can be utilised to produce improved spat...

Smart grid paradigm is hailed as the Holy Grail to manage the future electricity consumption in a sustainable manner, and demand response (DR) is a fundamental component in the realization of smart grids. However, DR requires active household participation and in the previous studies monetary benefit is identified as the main motivation for the hou...

We consider geodesic distance transformations for digital images. Given a M × N digital image, a distance image is produced by evaluating local pixel distances. Distance Transformation on Curved Space (DTOCS) evaluates shortest geodesics of a given pixel neighborhood by evaluating the height displacements between pixels. In this paper, we propose a...

In this paper, we address the epidemiology and morphology questions of breast cancer with special focus on different cell features created by lesions. In addition, we provide an insight into feature extraction and classification schemes in the image analysis pipeline. Based on our conducted research work, a novel feature extraction approach, a modi...

Accurate forecasting of loads is essential for smart grids and energy markets. This paper compares the performance of the following models in short-term load forecasting: 1) smart metering data based profile models, 2) a neural network (NN) model, and 3) a Kalman-filter based predictor with input nonlinearities and a physically based main structure...

In this paper, the performance of three Distance Transform on Curved Space-based features derived from digital H&E stained oncopathological images used in breast cancer pattern recognition scheme are compared. The three features utilized are SW-DTOCS, SW-WDTOCS and SW-3-4-DTOCS with three different sliding window (SW) sizes. The results imply that...

The building sector is a major energy consumer and CO2 emitter, being responsible for approximately 40% of the total consumption in the EU. Active demand side participation of electricity customers is seen as crucial in the management and reduction of the building sector's CO2 emissions. However, today's electricity markets are often lacking strong...

Electric vehicles and hybrids are expected to become increasingly common in the coming years. The implications of growing adoption depend on its geographical extent. For instance, vehicles that are chargeable from the electrical grid, such as plug-in hybrids, can introduce problems for the distribution network especially if the vehicle adoption is...

Different registries of the society are constantly opening for free usage and can be utilized to enhance spatial load modelling needed in strategic planning of electricity distribution networks. The main innovation of this paper is that of demonstrating possibilities of public geographic data, namely socio-economic, building and meteorological data...

Modelling of controllable loads is a necessary function required by demand side management, and specifically load control of smart grids. A large amount of smart metering data and other supporting data are available, enabling the development of new, intelligent data-driven fashions for recognising and modelling loads. However, it is a challenge to...

A regional model for sustainable biogas electricity production was formulated and tested for a Finnish province, North-Savo. By using the model the aim was to support decision making for reducing greenhouse gas (GHG) emissions and increasing renewable energy (RE) production in the studied region in the biogas electricity production system. The syst...

REMOWE – Regional Mobilizing of Sustainable Waste-to-Energy Production. This report presents some results of the REMOWE project and sets guidelines for regional policy makers, SME’s and the general public as well for more deep realization of waste-to-energy policy principles in action. The overall objective of the project is, on regional levels, to...

Methods for regional load prediction, capable of dealing with user-defined scenarios, are required in planning and managing electricity distribution networks. In this paper, the concept of scenario based tool is presented for the prediction of regional electricity loads in heating system scenarios. An innovation of the tool is that of self-organizi...

Airborne pollen have been associated with allergic symptoms in sensitized individuals, having a direct impact on the overall quality of life of a considerable fraction of the population. Therefore, forecasting elevated airborne pollen concentrations and communicating this piece of information to the public are key issues in prophylaxis and safeguar...

The recent technological developments monitoring the electricity use of small customers provides with a whole new view to develop electricity distribution systems, customer-specific services and to increase energy efficiency. The analysis of customer load profile and load estimation is an important and popular area of electricity distribution techn...

We describe a neural network model of a municipal wastewater treatment plant (WWTP) in which on-line total solids (TS) sewer data generated by a novel microwave sensor is used as a model input variable. The predictive performance of the model is compared with and without sewer data and with modelling with a traditional linear multiple linear regres...

Parametric and nonparametric modeling methods have been widely used for the estimation of forest attributes from airborne laser-scanning data and aerial photographs. However, the methods adopted suffered from complex remote-sensed data structures involving high dimensions, nonlinear relationships, different statistical distributions, and outliers....

The number of people in a certain place in desired time period is one of the main questions in many monitoring and management applications. Such information is needed also in tourism which has been one of most growing business areas in recent years and it have become a more important area of the service sector. Thus effective and sustainable manage...

Understanding and forecasting urban Air Quality (AQ) is not only a multi- faceted and computationally challenging problem for machine learning a l- gorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services poses the need for robust, efficient...

Handling of missing eddy covariance (EC) data is necessary to construct daily and annual sums of net ecosystem CO2 exchange (NEE). This study aims at evaluating three different types of artificial neural network methods (ANNs), namely multi-layer perceptron (MLP), support vector regression (SVR) and self organizing map (SOM), for the estimation of...

This study presents a QSAR/QSPR modelling and chemical grouping (read-across) approach to provide information on the biological properties of a group of aliphatic ethers, with accurate biological predictions restricted to those physico-chemical and (eco)toxicological properties where the performance of QSAR/QSPR has been shown to be acceptable. The...

The aim of this paper was to evaluate genetic algorithms (GA) and sensitivity analysis (SA) for selecting inputs of a multi-layer perceptron model (MLP) applied to forecast time-series of urban air pollutant. The main objective was to compare usability and efficiency of the methods. The results in general showed that the methods based on the SA and...

The collection of waste is a highly visible and important municipal service that involves large expenditures. Waste collection problems are, however, one of the most difficult operational problems to solve. This paper describes the optimization of vehicle routes and schedules for collecting municipal solid waste in Eastern Finland. The solutions ar...

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated and evaluated for the forecasting in time of urban pollutant concentrations. The forecasts of the combination of the MLP and HIRLAM models are compared with the correspondi...

In this paper, an integrated modeling system based on a multi-layer perceptron model is developed and evaluated for the forecasting of urban airborne maximum pollutant concentrations. In the first phase, the multi-objective genetic algorithm (MOGA) and sensitivity analysis are used in combination for identifying feasible system inputs. In the secon...

Methods for data imputation applicable to air quality data sets were evaluated in the context of univariate (linear, spline and nearest neighbour interpolation), multivariate (regression-based imputation (REGEM), nearest neighbour (NN), self-organizing map (SOM), multi-layer perceptron (MLP)), and hybrid methods of the previous by using simulated m...

The aim of the studies were to evaluate the usability of self-organizing maps (SOM) and Sammon's map- ping in analysing data originating from processes of environmental informatics and bioinformatics. The methods used in this work are first shortly introduced. The results show that the combination of SOM and Sammon's mapping has great potential in...

The modelling of real-world processes such as air quality is generally a difficult task due to both their chaotic and non-linear phenomenon and high dimensional sample space. Despite neural networks (NN) have been used successfully in this domain, the selection of network architecture is still problematic and time consuming task when developing a m...

Urban air pollutants have emerged as a severe problem which causes health effects and even premature deaths among sensitive groups. Therefore a warning system for air pollution episodes is widely needed to minimize negative health effects. However the forecasting of air pollution episodes has been observed to be problematic partly due their rarenes...

The objective of the study was to find a useful missing data imputing method for air quality forecasting applications. The univariate methods studied were the linear interpolation, spline and nearest neighbour (univariate) interpolation. Multivariate methods studied were multivariate nearest neighbour (NN), Self-Organising Map (SOM) and Multi-Layer...