Energy efficiency measures in buildings and alternative heating methods have led to a decreased demand for district heating (DH). Furthermore, due to a recent increase in extreme weather events, it is harder for DH providers to maintain a steady production leading to increased costs. These issues have led DH companies to change their price models. This thesis investigated such a price model change, made by Mälarenergi (ME) on the 1st of August 2018. The aim was to compare the old price model (PM1) with the new price model (PM2) by investigating the choice of base and peak loads a customer can make for the upcoming year, and/or if they should let ME choose for them. A prediction method, based on predicting the hourly DH demand, was chosen after a literature study and several method comparisons were made from using weather parameters as independent variables. Consumption data from Mälarenergi for nine customers of different sizes were gathered, and eight weather parameters from 2014 to 2018 were implemented to build up the prediction model. The method comparison results from Unscrambler showed that multilinear regression was the most accurate statistical modelling method, which was later used for all predictions. These predictions from Unscrambler were then used in MATLAB to estimate the total annual cost for each customer and outcome. For PM1, the results showed that the flexible cost for the nine customers stands for 76 to 85 % of the total cost, with the remaining cost as fixed fees. For PM2, the flexible cost for the nine customers stands for 46 to 61 % of the total cost, with the remaining as fixed cost. Regarding the total cost, PM2 is on average 7.5 % cheaper than PM1 for smaller customer, 8.6 % cheaper for medium customers and 15.9 % cheaper for larger customers. By finding the lowest cost case for each customer their optimal base and peaks loads were found and with the use of a statistical inference method (Bootstrapping) a 95 % confidence interval for the base load and the total yearly cost with could be established. The conclusion regarding choices is that the customer should always choose their own base load within the recommended confidence interval, with ME’s choice seen as a recommendation. Moreover, ME should always make the peak load choice because they are willing to pay for an excess fee that the customer themselves must pay otherwise.

Master thesis in cooperation with High Voltage Valley (Ludvika) and VB Energi (Ludvika)

Flera studier har visat att vädret har en inverkan på både vad vi konsumerar och när vi väljer att konsumera. Därför undersöktes antagandet att vädret kan påverka fler aspekter av våra liv, så som rörelsemönster. Idén att relatera det till FM kom på grund av att många fastighetsbolag går igenom en digitaliseringsfas för tillfället och därmed uppkommer behovet av att implementera nya typer av lösningar. Fastigheter har tidigare legat efter i digitaliseringsarbetet i kombination med att de inte har kunnat påverka sin inkomst förutom att höja sina årshyror. Därför har de potential att dra mest nytta av ny digitalisering, både när de kommer till effektivitet och ekonomisk vinning. Studien bygger på fastigheten Skrapan i Stockholm, där ett företag som heter Optiqo arbetar med att optimera fastighetens facility management. En del av deras jobb har varit att samla in data om användning av hygienutrymmen och optimera det städschemat. Optiqo har redan visat stor intjäningspotential samt implementerat en första version av detta, där en sensor mäter hur många personer som besökte toaletterna och sedan skickar en arbetsorder för städning när ett visst antal uppnås. Syftet med studien är att undersöka om fler parametrar skulle kunna implementeras in i detta system för att ännu bättre förutsäga och optimera städschemat. I denna rapport undersöks väder och andra potentiella användningsområden diskuteras. Väderdata har samlats in från SMHI och sedan jämförts med aktivitetsdata från Optiqo för att försöka fastställa mönster och trender. Testerna gjordes genom manuell jämförelse i Excel tillsammans med statistikprogrammet SPSS. Resultaten visar en tydlig korrelation mellan temperatur och aktivitet, som var statistiskt resonabel, medan de andra parametrarna inte kunde förklaras till fullo statistiskt. Korrelationen skulle kunna användas för ytterligare studier eller till och med implementeras i mindre skalor redan idag. Användningsområdena kan sträcka sig från att erbjuda extra försäljning i en anläggning när fler människor förväntas besöka den, till att skapa en dynamisk gräns för när en anläggning behöver städas, beroende på hur nedsmutsad en yta blir under ett specifikt väderscenario. / Multiple studies have shown that weather has an impact on both what we consume and when we choose to consume. Therefore, the assumption that weather might affect more aspects of our life, like movement patterns, was investigated. The idea to relate it to facility management came due to many real estate companies going through a digitalization phase and thus the need to implement new types of solutions. Real estates are also known for not being too involved in the digitalization in the past, as well as not being able to affect their income in many ways except raising their annual rents. Hence, they have the potential to benefit the most from new digital solutions, both in effectiveness and financial gain. The study is based on the property Skrapan in Stockholm, where a company called Optiqo works with optimizing the facility management. One part of their job has been to collect data regarding usage of restrooms and optimize the cleaning schedule. Optiqo have already proven big earnings potential as well as implemented a first version of this, where sensors measure how many people visited the restrooms and then sends a working order when a set number is reached. The aim of the study is to investigate if more parameters could be worked into this system to better predict and optimize the cleaning schedule. In this report weather is being examined and other possible usages of these parameters discussed. Weather data has been collected from SMHI and then compared with the activity data provided by Optiqo to try to determine patterns and trends. The tests were made by a manual comparison in excel along with the statistical program SPSS. The results showed a clear correlation with temperature and activity that was statistically reasonable, while the other parameters could not be explained to the fullest statistically. The correlation could be used for further studies or even be implemented at smaller scales today. The usages could range from offering extra sales in a facility when more people are predicted to visit, to creating a dynamic limit for when a facility needs to be cleaned, depending on how soiled a surface gets based on the sought-after weather scenario.

Companies in the transporting sector are undergoing an important transformation of electrifyingtheir fleets to meet the industry’s climate targets. To meet customer’s requests, keep its marketposition, and to contribute to a sustainable transporting industry, Scania needs to be in frontof the evolution. One aspect of this is to attract customers by providing accurate information anddetecting customer’s opportunities for electrification. Understanding the natural behavior of weatherparameters and their impact on energy consumption is crucial for providing accurate simulations ofhow daily operations would appear with an electric truck. The aim of this thesis is to map weatherparameters impact on energy consumption and to get an understanding of the correlations betweenenergy consumption and dynamic weather data. ML and deep learning models have undergone training using historical data from operations per-formed by Scania’s Battery Electric Vehicles(BEV). These models have been assessed against eachother to ensure that they are robust and accurate. Utilizing the trained models ability to providereliable consumption predictions based on weather, we can extract information and patterns aboutconsumption derived from customised weather parameters. The results show several interesting correlations and can quantify the impact of weather parametersunder certain conditions. Temperature is a significant factor that has a negative correlation withenergy consumption while other factors like precipitation and humidity prove less clear results. Byinteracting parameters with each other, some new results were found. For instance, the effect ofhumidity is clarified under certain temperatures. Wind speed also turns out to be an importantfactor with a positive correlation to energy consumption.