Modelling electricity prices: from the state of the art to a draft of a new proposal

In the last decades a liberalization of the electric market has started; prices are now determined on the basis of contracts on regular markets and their behaviour is mainly driven by usual supply and demand forces. A large body of literature has been developed in order to analyze and forecast their evolution: it includes works with different aims and methodologies depending on the temporal horizon being studied. In this survey we depict the actual state of the art focusing only on the recent papers oriented to the determination of trends in electricity spot prices and to the forecast of these prices in the short run. Structural methods of analysis, which result appropriate for the determination of forward and future values are left behind. Studies have been divided into three broad classes: Autoregressive models, Regime switching models, Volatility models. Six fundamental points arise: the peculiarities of electricity market, the complex statistical properties of prices, the lack of economic foundations of statistical models used for price analysis, the primacy of uniequational approaches, the crucial role played by demand and supply in prices determination, the lack of clearcut evidence in favour of a specific framework of analysis. To take into account the previous stylized issues, we propose the adoption of a methodological framework not yet used to model and forecast electricity prices: a time varying parameters Dynamic Factor Model (DFM). Such an eclectic approach, introduced in the late ‘70s for macroeconomic analysis, enables the identification of the unobservable dynamics of demand and supply driving electricity prices, the coexistence of short term and long term determinants, the creation of forecasts on future trends. Moreover, we have the possibility of simulating the impact that mismatches between demand and supply have over the price variable. This way it is possible to evaluate whether congestions in the network (eventually leading black out phenomena) trigger price reactions that can be considered as warning mechanisms.

C-Vine copula mixture model for clustering of residential electrical load pattern data

The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents a novel finite mixture modeling framework based on C-vine copulas (CVMM) for carrying out consumer categorization. The superiority of the proposed framework lies in the great flexibility of pair copulas towards identifying multi-dimensional dependency structures present in load profiling data. CVMM is compared to other classical methods by using real demand measurements recorded across 2,613 households in a London smart-metering trial. The superior performance of the proposed approach is demonstrated by analyzing four validity indicators. In addition, a decision tree classification module for partitioning new consumers is developed and the improved predictive performance of CVMM compared to existing methods is highlighted. Further case studies are carried out based on different loading conditions and different sets of large numbers of households to demonstrate the advantages and to test the scalability of the proposed method

A Review of using Data Mining Techniques in Power Plants

Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration

Nuclear power : a risky endeavor or an opportunity for the future?

In an age where a worldwide climate strategy is needed to mitigate the effects of climate change and global warming, the use of nuclear power as a source of electricity generation is currently a topic of discussion, amidst debates over energy decarbonization and security. This dissertation examines, theoretically and empirically, the effects of the closures of nuclear power plants on electricity prices and mortality. Using state-level monthly data on temperature, mortality, demographics, and the electricity market, I perform an event study that quantifies the impact of the closure of five nuclear power plants that were nearby and stopped operating at roughly the same time. Following the shutdown of nuclear power plants, it is anticipated that energy prices will rise, making access to temperature-regulating devices less affordable and exposing more people to extreme temperatures, which is anticipated to increase mortality rates. Results show that in almost all regions, residential electricity expenditure and prices decreased and exogenously increased after the nuclear plants’ closure. As predicted, electricity demand is inelastic, particularly in the summer months, and increased following the nuclear plant shutdowns. Importantly, extreme temperatures positively affect mortality, and I estimate that the coinciding exposure to them and the lower level of electricity expenditure have caused up to additional 2776 deaths per year. This evidence has important implications for the assessment of the effects of the USA’s decision to partially move away from nuclear power and the design of accompanying measures.Numa época em que uma política climática mundial é necessária para mitigar os efeitos das alterações climáticas e do aquecimento global, a utilização da energia nuclear como mecanismo para a produção de eletricidade é, atualmente, um tema em discussão, inserido nos debates sobre a descarbonização da produção de eletricidade e a independência energética. A presente dissertação examina, em termos teóricos e empíricos, o efeito do encerramento de centrais nucleares na mortalidade e no preço e consumo de energia elétrica. Usando dados mensais, ao nível estadual, relativos a temperatura, mortalidade, demografia e ao mercado da eletricidade, desenvolvo um event study que quantifica o impacto do encerramento de cinco centrais nucleares geograficamente próximas, cujas operações cessaram aproximadamente na mesma altura. Na sequência deste evento, espera-se que a energia se torne mais dispendiosa, expondo mais pessoas a temperaturas extremas, com potenciais consequências para a mortalidade. Os resultados indicam que, em quase todas as regiões, o preço e consumo de eletricidade no mercado residencial respetivamente aumentaram de forma exógena e diminuíram, após o encerramento das centrais nucleares. A elasticidade da procura de eletricidade é inelástica, particularmente nos meses de verão, tendo aumentado aquando do encerramento das centrais nucleares. Finalmente, mostro que temperaturas extremas fazem a mortalidade subir, e estimo que a exposição às mesmas, coincidindo com um menor consumo de eletricidade, tenham causado 2776 óbitos adicionais anualmente. Esta evidência tem implicações importantes para a avaliação dos efeitos da decisão dos EUA sobre a continuidade da energia nuclear e para o desenho de políticas públicas

Machine Learning Applications for Load Predictions in Electrical Energy Network

In this work collected operational data of typical urban and rural energy network are analysed for predictions of energy consumption, as well as for selected region of Nordpool electricity markets. The regression techniques are systematically investigated for electrical energy prediction and correlating other impacting parameters. The k-Nearest Neighbour (kNN), Random Forest (RF) and Linear Regression (LR) are analysed and evaluated both by using continuous and vertical time approach. It is observed that for 30 minutes predictions the RF Regression has the best results, shown by a mean absolute percentage error (MAPE) in the range of 1-2 %. kNN show best results for the day-ahead forecasting with MAPE of 2.61 %. The presented vertical time approach outperforms the continuous time approach. To enhance pre-processing stage, refined techniques from the domain of statistics and time series are adopted in the modelling. Reducing the dimensionality through principal components analysis improves the predictive performance of Recurrent Neural Networks (RNN). In the case of Gated Recurrent Units (GRU) networks, the results for all the seasons are improved through principal components analysis (PCA). This work also considers abnormal operation due to various instances (e.g. random effect, intrusion, abnormal operation of smart devices, cyber-threats, etc.). In the results of kNN, iforest and Local Outlier Factor (LOF) on urban area data and from rural region data, it is observed that the anomaly detection for the scenarios are different. For the rural region, most of the anomalies are observed in the latter timeline of the data concentrated in the last year of the collected data. For the urban area data, the anomalies are spread out over the entire timeline. The frequency of detected anomalies where considerably higher for the rural area load demand than for the urban area load demand. Observing from considered case scenarios, the incidents of detected anomalies are more data driven, than exceptions in the algorithms. It is observed that from the domain knowledge of smart energy systems the LOF is able to detect observations that could not have detected by visual inspection alone, in contrast to kNN and iforest. Whereas kNN and iforest excludes an upper and lower bound, the LOF is density based and separates out anomalies amidst in the data. The capability that LOF has to identify anomalies amidst the data together with the deep domain knowledge is an advantage, when detecting anomalies in smart meter data. This work has shown that the instance based models can compete with models of higher complexity, yet some methods in preprocessing (such as circular coding) does not function for an instance based learner such as k-Nearest Neighbor, and hence kNN can not option for this kind of complexity even in the feature engineering of the model. It will be interesting for the future work of electrical load forecasting to develop solution that combines a high complexity in the feature engineering and have the explainability of instance based models.publishedVersio

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Probabilistic Wind Power and Electricity Load Forecasting with Echo State Networks

With the introduction of distributed generation and the establishment of smart grids, several new challenges in energy analytics arose. These challenges can be solved with a specific type of recurrent neural networks called echo state networks, which can handle the combination of both weather and power consumption or production depending on the dataset to make predictions. Echo state networks are particularly suitable for time series forecasting tasks. Having accurate energy forecasts is paramount to assure grid operation and power provision remains reliable during peak hours when the consumption is high. The majority of load forecasting algorithms do not produce prediction intervals with coverage guarantees but rather produce simple point estimates. Information about uncer- tainty and prediction intervals is rarely useless. It helps grid operators change strategies for configuring the grid from conservative to risk-based ones and assess the reliability of operations. A popular way of producing prediction intervals in regression tasks is by applying Bayesian regression as the regression algorithm. As Bayesian regression is done by sampling, it nat- urally lends itself to generating intervals. However, Bayesian regression is not guaranteed to satisfy the designed coverage level for finite samples. This thesis aims to modify the traditional echo state network model to produce marginally valid and calibrated prediction intervals. This is done by replacing the standard linear regression method with Bayesian linear regression while simultaneously reducing the di- mensions to speed up the computation times. Afterward, a novel calibration technique for time series forecasting is applied in order to obtain said valid prediction intervals. The experiments are conducted using three different time series, two of them being a time series of electricity load. One is univariate, and the other is bivariate. The third time series is a wind power production time series. The proposed method showed promising results for all three datasets while significantly reducing computation times in the sampling ste

Unwrapping the Consequences: Exploring Consumer Behavior during Norway's Economic Downturn and its Influence on Christmas Trade

Abstract Purpose: The aim of the research is to see if the economic downturn has had an impact on Christmas trade. The downturn in question is the period in Norway following the start of the war in Ukraine, which includes interest rate increases, increased electricity, food, and fuel prices, and overall increasing inflation from 2022 to the present. There is reason to believe that the high prices have had an impact on the Christmas trade in 2022. However, Christmas shopping is culturally important for many people, which may lead to people not altering their budgets even during an economic downturn. Problem statement: Has the downturn affected Christmas gift spending? Design/research methods/approach: A descriptive research design was used to investigate the topic. Using a quantitative method, an anonymous online survey was conducted and published to social networking sites (SNSs). The answers were later analyzed in SPSS, through univariate (Spearman correlation coefficient) and multivariate (binomial logistic regression) tests that provided objective results. Findings: One out of four hypotheses were supported. There was significant evidence that the Christmas spending was affected by the downturn but less affected than usual consumption. Significance was also shown in the relation between materialism and Christmas spending adjustment to downturn, however, the relationship is positive which contradicted the hypothesis and was not expected. The remaining hypotheses that proposed the relationship between reciprocation and altruism with Christmas spending adjustment to downturn did not show significance in the binomial logistic regression. Implications: Both the individual consumer and companies can use this research to gain a broader understanding of their own situation. It is important for consumers to understand how Christmas shopping can affect their financial state, especially during a downturn. Many companies are reliant on sales during Christmas, and for them it is crucial to know their consumers' behaviors. This is of high relevance for those who work with consumer protection as well. Keywords: Consumer behavior, downturn, Christmas, gift giving, reciprocation, altruism, materialis

Unwrapping the Consequences: Exploring Consumer Behavior during Norway's Economic Downturn and its Influence on Christmas Trade

Abstract Purpose: The aim of the research is to see if the economic downturn has had an impact on Christmas trade. The downturn in question is the period in Norway following the start of the war in Ukraine, which includes interest rate increases, increased electricity, food, and fuel prices, and overall increasing inflation from 2022 to the present. There is reason to believe that the high prices have had an impact on the Christmas trade in 2022. However, Christmas shopping is culturally important for many people, which may lead to people not altering their budgets even during an economic downturn. Problem statement: Has the downturn affected Christmas gift spending? Design/research methods/approach: A descriptive research design was used to investigate the topic. Using a quantitative method, an anonymous online survey was conducted and published to social networking sites (SNSs). The answers were later analyzed in SPSS, through univariate (Spearman correlation coefficient) and multivariate (binomial logistic regression) tests that provided objective results. Findings: One out of four hypotheses were supported. There was significant evidence that the Christmas spending was affected by the downturn but less affected than usual consumption. Significance was also shown in the relation between materialism and Christmas spending adjustment to downturn, however, the relationship is positive which contradicted the hypothesis and was not expected. The remaining hypotheses that proposed the relationship between reciprocation and altruism with Christmas spending adjustment to downturn did not show significance in the binomial logistic regression. Implications: Both the individual consumer and companies can use this research to gain a broader understanding of their own situation. It is important for consumers to understand how Christmas shopping can affect their financial state, especially during a downturn. Many companies are reliant on sales during Christmas, and for them it is crucial to know their consumers' behaviors. This is of high relevance for those who work with consumer protection as well. Keywords: Consumer behavior, downturn, Christmas, gift giving, reciprocation, altruism, materialis