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This paper seeks to provide an alternative forecast to that provided by the Energy Information Administration (EIA) on energy-related monthly CO₂ emissions in the United States. The data on CO₂ emissions from petroleum, natural gas, coal and total fossil fuels obtained via the EIA covering the period January 2005 to November 2013 is analysed and then forecasted using ARIMA, Holt-Winters, and Exponential Smoothing prior to introducing the Singular Spectrum Analysis (SSA) technique for CO₂ emissions forecasting. A new combination forecast (EIA-SSA) is also introduced by merging the SSA and EIA forecasts, and is seen outperforming all models including the EIA forecast. Finally, the EIA-SSA model is used to provide an alternative 12 month ahead outlook for US energy-related CO₂ emissions from December 2013 to November 2014. This research is expected to influence the methodology adopted by the EIA for forecasting CO₂ emissions in the future by improving the accuracy of the forecasts, and the impact of this study will be clearer upon comparing the actual CO₂ emissions in US with the EIA, and EIA-SSA forecasts over the 12 month period which follows.

The ACF plays an important role in time series analysis as it is used for identifying lags in all autoregressive models. Given that energy continues to be modelled by classical time series methods which rely on the ACF, this paper aims to evaluate whether such models are valid as recent evidence suggests that the sum of the ACF is always equal to -½, which in turn indicates that a set of ACF estimates are not IID, and leaves open for criticism the theory underlying classical time series methods. The applicability of this new theory in the energy sector is evaluated via an application into four real data sets which include the effects of structural breaks, seasonality and unit root problems. The evidence shows that there exists a fundamental flaw in classical time series models used for energy data modelling.

Data Mining has revolutionized the modern world and is rapidly transforming into a key ingredient for the successful discovery and extraction of relationships, hidden patterns and trends in the energy sector. The emergence and availability of Big Data in the energy industry adds to the prolific importance of Data Mining techniques as they provide feasible solutions for mining and exploiting information crucial for management decision making and achieving productivity gains in the energy industry. This succinct review paper seeks to outline the various Data Mining techniques that have recently been adopted for solving energy related problems in the industry. The evidence suggests that a variety of Data Mining techniques have been evaluated for solving energy related problems in the recent past, and these include cluster analysis, classification trees, neural networks, genetic algorithm, dynamic regressions, Bayesian models, support vector machines, k-nearest neighbours, and random forests. This paper finds evidence supportive of the claim that cluster analysis and classification trees are the most frequently adopted Data Mining techniques in the energy industry.

The aim of this paper is to present a comparative study on the performance of the two different forecasting approaches of SSA in the presence of outliers. We examine this issue from different points of view. As our real data set, we have considered the well known WTI Spot Price series. The effect on forecasting process when confronted with outlier(s) in different parts of a time series is evaluated. Based on this study, we find evidence which suggests that the existence of outliers affect SSA reconstruction and forecasting results, and that VSSA forecasting performs better than RSSA in terms of the accuracy and robustness of forecasts.

The aim of this paper is to evaluate the impact of disaggregating the data on the performance of two different versions of SSA methods, namely RSSA and VSSA. Using monthly data for natural gas prices in the United States residential sector with an out-of-sample period of 10:2010–5:2015, given an in-sample period of 1:2002–9:2010 we find evidence which suggests that data disaggregation improves SSA performance in terms of the accuracy and robustness of forecasts.

A great effort is currently taking place to meet the increasing UK energy demand towards 2030 though an optimized generation mix that meets the renewable targets, and the peak demand as well as the greenhouse gas emissions intensity of 50 gCO₂eq/kWh through the investment in non-dispatchable generation, and Demand Side Management technology. In this report, an optimization model has been utilized with preliminary constraints involving Plant Load Factor and plant capacity of the technologies with a potential contribution in the 2030 mix. Eventually, three scenarios have been generated depending on National Grid — Gone Green, more CO₂ emissions constraint and Capital Cost Reduction.

This paper investigates three stochastic modelling procedures for generating $N$ (user specified) synthetic annual electricity demand profiles at one-minute resolution. The paper reviews previous work in the application of HMM for synthesizing highly stochastic time-series of domestic electricity demand through a sophisticated framework coalescing 480 distinct HMM. The efficiency of a proposed approach for integrating a time-series deseasonalizing technique with a single HMM has been studied in parallel with a compatible stochastic modeling framework of a time-series deseasonalized ARIMA model. Various statistical measures/characteristics of the real and synthetic profiles have been compared for all the three stochastic modelling procedures to identify the most efficient and practically suitable medium for generating synthetic electricity time-series at a fine temporal resolution. Results have been shown for both the individual buildings and the composite (aggregated) profiles of many buildings.