Global ETD Search

21	Natural gas storage level forecasting using temperature data Sundin, Daniel January 2020 (has links) Even though the theory of storage is historically a popular view to explain commodity futures prices, many authors focus on the oil price link. Past studies have shown an increased futures price volatility on Mondays and days when natural gas storage levels are released, which could both implicate that storage levels and temperature data are incorporated in the prices. In this thesis, the U.S. natural gas storage level change is studied as a function of the consumption and production. Consumption and production are furthered segmented and separately forecasted by modelling inverse problems that are solved by least squares regression using temperature data and timeseries analysis. The results indicate that each consumer consumption segment is highly dependent of the temperature with R2-values of above 90%. However, modelling each segment completely by time-series analysis proved to be more efficient due to lack of flexibility in the polynomials, lack of used weather stations and seasonal patterns in addition to the temperatures. Although the forecasting models could not beat analysts’ consensus estimates, these present natural gas storage level drivers and can thus be used to incorporate temperature forecasts when estimating futures prices. Natural gas storage forecasting Natural gas storage Natural gas Storage forecasting Forecasting Futures Futures forecasting Natural gas futures forecasting Least squares regression Regression Machine learning Exponential Weighted Moving Average Moving Average EWMA MA Seasonality Commodity futures Optimisation Inverse problems Consumption forecasting Production Forecasting Residential Commercial Industrial Electric power NOAA EIA Pipelines Polynomial Weather stations Mathematics Matematik
22	Feature selection in short-term load forecasting / Val av attribut vid kortvarig lastprognos för energiförbrukning Söderberg, Max Joel, Meurling, Axel January 2019 (has links) This paper investigates correlation between energy consumption 24 hours ahead and features used for predicting energy consumption. The features originate from three categories: weather, time and previous energy. The correlations are calculated using Pearson correlation and mutual information. This resulted in the highest correlated features being those representing previous energy consumption, followed by temperature and month. Two identical feature sets containing all attributes1 were obtained by ranking the features according to correlation. Three feature sets were created manually. The first set contained seven attributes representing previous energy consumption over the course of seven days prior to the day of prediction. The second set consisted of weather and time attributes. The third set consisted of all attributes from the first and second set. These sets were then compared on different machine learning models. It was found the set containing all attributes and the set containing previous energy attributes yielded the best performance for each machine learning model. 1In this report, the words ”attribute” and ”feature” are used interchangeably. / I denna rapport undersöks korrelation och betydelsen av olika attribut för att förutspå energiförbrukning 24 timmar framåt. Attributen härstammar från tre kategorier: väder, tid och tidigare energiförbrukning. Korrelationerna tas fram genom att utföra Pearson Correlation och Mutual Information. Detta resulterade i att de högst korrelerade attributen var de som representerar tidigare energiförbrukning, följt av temperatur och månad. Två identiska attributmängder erhölls genom att ranka attributen över korrelation. Tre attributmängder skapades manuellt. Den första mängden innehåll sju attribut som representerade tidigare energiförbrukning, en för varje dag, sju dagar innan datumet för prognosen av energiförbrukning. Den andra mängden bestod av väderoch tidsattribut. Den tredje mängden bestod av alla attribut från den första och andra mängden. Dessa mängder jämfördes sedan med hjälp av olika maskininlärningsmodeller. Resultaten visade att mängden med alla attribut och den med tidigare energiförbrukning gav bäst resultat för samtliga modeller. Short-term load forecasting energy consumption forecasting Linear regression SVR Random Forest machine learning regression feature selection attribute selection Pearson correlation Mutual information correlation matrix Two-way ANOVA Tukey’s HSD test. Kortsiktig lastprognos Energiförbrukningsprognos Linjär regression SVR Random forest Maskininlärning Attributval Pearson-korrelation Ömsesidig information Korrelationsmatris Tvåvägs ANOVA Tukey’s HSD-test. Computer and Information Sciences Data- och informationsvetenskap

Search results

Natural gas storage level forecasting using temperature data

Feature selection in short-term load forecasting / Val av attribut vid kortvarig lastprognos för energiförbrukning