Past and future emissions in the Norwegian economy : a combined machine learning and index decomposition analysis

This thesis concerns the past and future emissions within the Norwegian economy. It employs retrospective analysis to analyse past sectoral emissions on a national level and supervised machine learning to predict emissions 5 years ahead for Norwegian municipalities. Policies addressing GHG-emissions have been on the agenda for over 30 years, yet sectoral emissions from Norwegian economic activity have risen 10.4% compared to the 1990-level. Most retrospective analysis carried out on emissions only analyses trends and tentative causes. Underlying drivers of emissions therefore remain unquantified, and their magnitude remains unknown. A logarithmic mean divisia index decomposition analysis is provided on sectoral emissions from 10 economic sectors in the period 1990 – 2019 alleviate the problem/provide answers on this area. The analysis shows that economic growth and worsening energy efficiency, particularly in the transport and petroleum & mining sectors, have contributed to a net increase of 6218 mktCO2e in emissions. Results also show that changes in economic structure, decreased usage of fossil fuels and increased carbon efficiency have worked as abating factors, but that they are outweighed by the factors which contribute to increase in emissions. Given Norway’s ambition of curbing its own emissions by a significant degree by 2030 and then net zero by 2050. While these goals are specific only to certain types of emissions, it is still a somewhat open question as to how emissions might develop in the near future. A supervised machine learning analysis was carried out on GHG emissions from 354 municipalities in the period 2009 – 2019. An architecture using univariate linear regression tests for variable selection and extreme gradient boosting for prediction on a panelised dataset of emissions provided the lowest prediction error and projects that emissions from Norwegian municipalities will fall, reaching a level of 33 470 mktCO2e in 2025.submittedVersionM-ECO

A review on Day-Ahead Solar Energy Prediction

Accurate day-ahead prediction of solar energy plays a vital role in the planning of supply and demand in a power grid system. The previous study shows predictions based on weather forecasts composed of numerical text data. They can reflect temporal factors therefore the data versus the result might not always give the most accurate and precise results. That is why incorporating different methods and techniques which enhance accuracy is an important topic. An in-depth review of current deep learning-based forecasting models for renewable energy is provided in this paper

Empirical Analysis of Natural Gas Markets

Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books

Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions

Modeling the effect of blending multiple components on gasoline properties

Global CO2 emissions reached a new historical maximum in 2018 and transportation sector contributed to one fourth of those emissions. Road transport industry has started moving towards more sustainable solutions, however, market penetration for electric vehicles (EV) is still too slow while regulation for biofuels has become stricter due to the risk of inflated food prices and skepticism regarding their sustainability. In spite of this, Europe has ambitious targets for the next 30 years and impending strict policies resulting from these goals will definitely increase the pressure on the oil sector to move towards cleaner practices and products. Although the use of biodiesel is quite extended and bioethanol is already used as a gasoline component, there are no alternative drop-in fuels compatible with spark ignition engines in the market yet. Alternative feedstock is widely available but its characteristics differ from those of crude oil, and lack of homogeneity and substantially lower availability complicate its integration in conventional refining processes. This work explores the possibility of implementing Machine Learning to develop predictive models for auto-ignition properties and to gain a better understanding of the blending behavior of the different molecules that conform commercial gasoline. Additionally, the methodology developed in this study aims to contribute to new characterization methods for conventional and renewable gasoline streams in a simpler, faster and more inexpensive way. To build the models included in this thesis, a palette with seven different compounds was chosen: n-heptane, iso-octane, 1-hexene, cyclopentane, toluene, ethanol and ETBE. A data set containing 243 different combinations of the species in the palette was collected from literature, together with their experimentally measured RON and/or MON. Linear Regression based on Ordinary Least Squares was used as the baseline to compare the performance of more complex algorithms, namely Nearest Neighbors, Support Vector Machines, Decision Trees and Random Forest. The best predictions were obtained with a Support Vector Regression algorithm using a non-linear kernel, able to reproduce synergistic and antagonistic interaction between the seven molecules in the samples