Deeper customer insight from NPS-questionnaires with text mining - Comparison of Machine, Representation and Deep Learning models in Finnish language sentiment classification

The amount of data in the world has grown significantly during the last decade. It has been estimated that around 90 percent of all this data was generated in the last two years alone, and the pace of data creation is constantly accelerating. According to a popular belief around 80 percent of all the data in the world is in unstructured form. Unstructured data is heterogeneous and in difficult to analyze formats such as video, audio, image and text. This kind of data generally lack the tabular structure required by computers to effortlessly analyze the data. This thesis concentrates on the field of text mining, also known as text analytics. Text mining refers to techniques that can be used to extract information from textual unstructured data. Text mining is currently one of the most popular emerging analytics methods. It can be used in numerous ways to bring business value to companies from various fields. Text mining can be for instance used to gain deeper customer understanding by analysis of texts written by current and possible future customers. Deep customer understanding is a crucial part of the foundation on which successful consumer businesses are built on. This is one of the reasons Telia Finland is interested in the possibilities of text mining. In this thesis I will cover how text mining can be used to bring business value and how text mining can be used to gain deeper customer insight from NPS-questionnaires. Telia uses the popular Net Promoter Score (NPS) metric to assess customer satisfaction and loyalty. Telia Finland currently uses a SaaS application in the analysis of textual feedbacks connected to NPS questionnaires in order to gain more customer insight. Telia Finland is however interested in a more versatile, agile and better performing text mining solution than the current SaaS. This thesis aims to discover whether it would be possible for Telia to insource text analytics without notable performance compromises. In this thesis sentiment classification models are programmed with Python using all modern approaches to text mining, that is with machine, representation and deep learning approaches. The models use manually labelled Finnish language NPS feedback data from Telia Finland as the training and testing datasets. The classification performance of these models is evaluated by quantitative methods and compared to the performance of the currently used SaaS solution. All of the developed final sentiment classification models outperformed the current solution in overall sentiment classification of Finnish language NPS-feedbacks. When comparing the performance of the models, the model using the deep learning approach outperformed other approaches. The tuned deep learning model utilizing Long Short-Term Memory networks reached a classification accuracy and class-averaged precision of around 80 percent

Economic potential of explicit demand response in private electric vehicle charging networks

Suomen pitkän aikavälin tavoitteena on olla hiilineutraali yhteiskunta. Tähän pyritään mm. lisäämällä uusiutuvaa energiantuotantoa, parantamalla energiatehokkuutta ja kasvattamalla sähköautojen määrää. Sähköautojen sekä vaihtelevan uusiutuvan energiatuotannon lisääntyminen aiheuttaa kuitenkin haasteita sähköjärjestelmän tehotasapainon ylläpidolle. Sähköjärjestelmässä sähkön kysynnän ja tarjonnan on kohdattava joka hetki, eli sähköä on tuotettava ja kulutettava samaan tahtiin, jotta sähköverkon taajuus pysyy vakiona. Tätä tasapainoa voidaan ylläpitää säätämällä joko tuotannon, kysynnän tai näiden molempien määrää. Sähkön kysynnän määrän säätämistä kutsutaan usein kysyntäjoustoksi. Tämä diplomityö on toteutettu osana EU:n SysFlex-tutkimushanketta, jonka tarkoituksena on kehittää joustavia ratkaisuja uusiutuvan energiantuotannon lisäämisen sähköverkolle aiheuttamiin haasteisiin. Diplomityö käsittelee eksplisiittisen kysyntäjouston hyötyjä sekä taloudellista potentiaalia sähköautojen yksityisessä latausverkostossa. Joustopotentiaalia arvioidaan kvantitatiivisesti Liikennevirta Oy:n hallinnoimilla yksityisillä latauspisteillä tapahtuneiden lataustapahtumien kautta. Joustopotentiaalin määrä, sekä arvo Fingridin taajuusohjatuilla häiriöreservimarkkinoilla, ratkaistiin tarkoitukseen kehitettyjä kaavoja hyödyntäen Pythonilla. Tulevaisuuden joustokapasiteetin ja sen taloudellisen potentiaalin arviointia varten työssä kehitettiin myös simulaatiomalli, joka poimii satunnaisotoksen tutkimusaineistosta, säilyttäen lataustapahtumien eri attribuuttien väliset riippuvuussuhteet kopuloiden avulla, sekä laskee näiden lataustapahtumien joustopotentiaalit. Lisäksi deskriptiivisen data-analyysin kautta pyrittiin hahmottamaan yleiskuvaa sähköautojen latauskäyttäytymisestä yksityisillä latauspisteillä sekä analysoimaan mm. lataustapahtumien jakaumaa ajallisesti sekä eri asiakassegmenttien kesken. Tutkimuksen perusteella Suomen koko yksityisen latausverkoston jouston taloudellinen potentiaali olisi vuonna 2017 ollut noin 9 600 € FCR-D markkinoilla, mikäli koko sähköautokanta käyttäytyisi aineiston keskiarvoisen asiakkaan kaltaisesti. Mikäli hallituksen tavoite neljännesmiljoonasta sähköautosta vuonna 2030 toteutuu, kasvaisi verkoston jouston taloudellinen potentiaali mahdollisesti jopa yli 4,7 miljoonaan euroon vuodessa. Sähköautokanta tulee tulevaisuudessa muodostamaan helposti hyödynnettävän ja suuren kysyntäjoustoresurssin, jonka taloudellinen potentiaalikin tulee kasvamaan merkittäväksi. Tällä hetkellä suurin este yksityisten sähköautojen lataustapahtumien hyödyntämiselle joustoresurssina on älykkäiden latauspisteiden pieni määrä, valtaosa Suomen sähköautojen latauksista tapahtuu vielä tavanomaisten kotitalouspistokkeiden kautta.The long-term goal of Finland is to be a carbon neutral society. This is to be achieved e.g. by increasing renewable energy production, by improving overall energy efficiency and by increasing the number of electric vehicles significantly. The increase in the number of electric vehicles and intermittent energy production however poses challenges to the power system. In the power system there has to be a balance between production and consumption of electricity at all times. This balance can be maintained by adjusting the production or consumption, or both. The adjustment of power consumption to better match with power supply can also be called demand response. This master’s thesis has been carried out as a part of the EU's SysFlex research project, which aims to identify solutions to issues associated with integrating large-scale renewable energy into the energy mix. The thesis addresses the economic potential and benefits of explicit demand response in private electric vehicle charging networks. Flexibility potential is assessed quantitatively by examining EV charging events carried out at private charging points managed by Liikennevirta Oy. The flexibility potential of the charging events, as well as their values in Finnish TSO Fingrid’s frequency containment reserves, are solved using Python and the formulas developed in this thesis. The future economic potential of private EV charging network was assessed also by a simulation model that uses copulas to sample charging events from the original data and calculates their flexibility potentials. Through descriptive data-analysis insights were also sought about the charging behaviour of different customer segments and the different distributions of charging events at private charging points. According to this study, the economic potential of the entire Finnish private charging network in Fingrid’s FCR-D marketplace would have been around 9 600 € in 2017, if the entire Finnish electric car fleet behaved like the average customer in the research material. If the 2030 target of a quarter million EVs set by the Finnish government will be realized, the economic potential of explicit demand response in the private EV charging network could probably be as much as over 4.7 million euros per year. In the future, the EV fleet will be a sizable and highly practical demand response asset with a significant economic potential. At the present, the biggest obstacle in exploiting the full potential of EVs in explicit demand response is the small number of intelligent private charging points. Currently most of the home charging of Finnish EVs is still done via conventional household sockets

Multivariate copula procedure for electric vehicle charging event simulation

Funding Information: This work was supported by the Academy of Finland , STORE-project [grant number 298317 ]. Publisher Copyright: © 2021 The Author(s)This paper introduces a novel application area for multivariate copulas in electric vehicle charging event simulation and dependency analysis. We propose a multivariate copula procedure that can be used to generate new synthetic charging events, which retain the complex dependency and correlation structures present in real-world charging events. The paper compares the most popular multivariate copula functions to discover the most reliable one to be used with electric vehicle charging event data. Accurate EV charging event simulation and analysis is crucial in multiple theoretical and practical applications such as charging load and demand response aggregation modelling. Based on multiple goodness-of-fit tests and charging load profiles of simulated charging events, the Student-t copula was found to be the most reliable multivariate copula to be used with EV charging data. Overall, the multivariate copula procedure is effective in analysis and simulation of EV charging events as it retains the inherent variability and complex dependencies of real charging events.Peer reviewe

Explicit demand response potential in electric vehicle charging networks : Event-based simulation based on the multivariate copula procedure

Publisher Copyright: © 2022 The AuthorsThis paper proposes a novel combined event-based simulation model for assessing the explicit demand response potential of electric vehicle (EV) charging networks. The model utilizes different multivariate copulas in generation of realistic artificial charging events that effectively retain the complex dependency structures and parameter distributions of real data important for accurate demand response simulation. A deterministic model is used to estimate the maximal explicit demand response potential of individual charging events based on technical requirements of the frequency containment reserve for disturbance situations (FCR-D) market. The proposed model achieved a mean absolute percentage error (MAPE) of 3.27% when considering averaged daily dispatchable FCR-D potentials, and a MAPE of 4.65% in prediction of dispatchable FCR-D potential with one workweek of data. The results and methodology have been verified and validated with real life data and through comparison with a previous non-copula application for EV FCR profile estimation which it outperformed. The combined event-based simulation model can boost active participation of EVs in power network balancing and is suitable for use in various practical and theoretical applications.Peer reviewe

Stochastic Multicriteria Acceptability Analysis of EV Sharing in Nordic Rural Areas Affected by Seasonal Residence and Counterurbanization

Sharing economy and shared mobility has become a widespread trend in urban areas worldwide. Due to lower population density, car sharing, and other shared mobility applications are generally not accessible in rural areas. This paper utilizes a Stochastic Multicriteria Acceptability Analysis (SMAA) method to assess the criteria importance in siting problem of rural electric vehicle (EV) sharing systems. Nordic rural areas are used as a case study in this analysis, and we compare their feasibility to act as a pilot location for EV sharing. Seasonal residence, rural tourism and counterurbanization are common themes in Nordic rural areas and act as enablers for rural vehicle sharing. Based on our novel application of SMAA to this context, we found that Swedish rural areas would be most suitable for a rural EV sharing pilot. High tourism and low vehicle ownership were identified to be the most important criteria for this siting problem.Peer reviewe

Interruptibility in private electric vehicle charging: Case Finland

Uncontrolled electric vehicle charging can increase peak loads and cause extra stress on the power system. By smartly controlling EV charging events, adversities caused by charging can be mitigated and charging loads can be utilized in different demand-side management schemes to support the power system on both local and grid levels. Electric vehicle charging events consist of the actual time spent charging and of idling, when the EV is connected to the EVSE but is not charging. This idling time can be utilized as an opportunity for load shifting or power reduction without compromising the charging outcome. This study analyses through simulation the interruptibility potential of Finnish private electric vehicle charging network. Based on the results roughly 70% of charging events can be interrupted for at least 10% of the time they are plugged-in to the EVSE.Peer reviewe

Digital transformation, well-being and shrinking communities: Narrowing the divides between urban and rural

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