Ensemble prediction model with expert selection for electricity price forecasting

Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets

The impact of macroeconomic leading indicators on inventory management

Forecasting tactical sales is important for long term decisions such as procurement and informing lower level inventory management decisions. Macroeconomic indicators have been shown to improve the forecast accuracy at tactical level, as these indicators can provide early warnings of changing markets while at the same time tactical sales are sufficiently aggregated to facilitate the identification of useful leading indicators. Past research has shown that we can achieve significant gains by incorporating such information. However, at lower levels, that inventory decisions are taken, this is often not feasible due to the level of noise in the data. To take advantage of macroeconomic leading indicators at this level we need to translate the tactical forecasts into operational level ones. In this research we investigate how to best assimilate top level forecasts that incorporate such exogenous information with bottom level (at Stock Keeping Unit level) extrapolative forecasts. The aim is to demonstrate whether incorporating these variables has a positive impact on bottom level planning and eventually inventory levels. We construct appropriate hierarchies of sales and use that structure to reconcile the forecasts, and in turn the different available information, across levels. We are interested both at the point forecast and the prediction intervals, as the latter inform safety stock decisions. Therefore the contribution of this research is twofold. We investigate the usefulness of macroeconomic leading indicators for SKU level forecasts and alternative ways to estimate the variance of hierarchically reconciled forecasts. We provide evidence using a real case study

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table

Discovery of motifs to forecast outlier occurrence in time series

The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. Outliers in time series can lead to unreliable modeling and poor forecasts. Therefore, the identification of future outlier occurrence is an essential task in time series analysis to reduce the average forecasting error. The main goal of this work is to predict the occurrence of outliers in time series, based on the discovery of motifs. In this sense, motifs will be those pattern sequences preceding certain data marked as anomalous by the proposed metaheuristic in a training set. Once the motifs are discovered, if data to be predicted are preceded by any of them, such data are identified as outliers, and treated separately from the rest of regular data. The forecasting of outlier occurrence has been added as an additional step in an existing time series forecasting algorithm (PSF), which was based on pattern sequence similarities. Robust statistical methods have been used to evaluate the accuracy of the proposed approach regarding the forecasting of both occurrence of outliers and their corresponding values. Finally, the methodology has been tested on six electricity-related time series, in which most of the outliers were properly found and forecasted.Ministerio de Ciencia y Tecnología TIN2007- 68084-C-00Junta de Andalucia P07-TIC- 0261

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Energy Forecasting in Smart Grid Systems: A Review of the State-of-the-art Techniques

Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL) considering different models and architectures. Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data pre-processing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption and American electric power (AEP) datasets is conducted to analyze the performance of point and probabilistic forecasting methods. The analysis demonstrates higher accuracy of the long-short term memory (LSTM) models with appropriate hyper-parameter tuning among point forecasting methods especially when sample sizes are larger and involve nonlinear patterns with long sequences. Furthermore, Bayesian bidirectional LSTM (BLSTM) as a probabilistic method exhibit the highest accuracy in terms of least pinball score and root mean square error (RMSE)

Stock Price Prediction Using the ARIMA Model

Stock price prediction is an important topic in finance and economics which has spurred the interest of researchers over the years to develop better predictive models. The autoregressive integrated moving average (ARIMA) models have been explored in literature for time series prediction. This paper presents extensive process of building stock price predictive model using the ARIMA model. Published stock data obtained from New York Stock Exchange (NYSE) and Nigeria Stock Exchange (NSE) are used with stock price predictive model developed. Results obtained revealed that the ARIMA model has a strong potential for short-term prediction and can compete favourably with existing techniques for stock price prediction

APARCH Models Estimated by Support Vector Regression

This thesis presents a comprehensive study of asymmetric power autoregressive conditional heteroschedasticity (APARCH) models for modelling volatility in financial return data. The goal is to estimate and forecast volatility in financial data with excess kurtosis, volatility clustering and asymmetric distribution. Models based on maximum likelihood estimation (MLE) will be compared to the kernel based support vector regression (SVR). The popular Gaussian kernel and a wavelet based kernel will be used for the SVR. The methods will be tested on empirical data, including stock index prices, credit spreads and electric power prices. The results indicate that asymmetric power models are needed to capture the asseymtry in the data. Furthermore, SVR models are able to improve estimation and forecasting accuracy, compared with the APARCH models based on MLE.Masteroppgave i statistikkSTAT399MAMN-STA

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

Essays on Energy Portfolio Management

Diese englischsprachige Dissertation behandelt ausgewählte Fragen zum Thema Portfoliomanagement in Energiemärkten. Im Kontext der modernen Portfoliotheorie werden theoretische Verteilungsannahmen untersucht, die einen optimalen Mittelwert-Varianz-Ansatz implizieren. Der Bereich zu Energiemärkten befasst sich einerseits mit Kurzfristprognosen von Day-Ahead-Preisen auf dem Strommarkt. Andererseits werden auf dem Erdgasmarkt die von komplexen Energiederivaten impliziten Volatilitäten analysiert. Einige interessante Beiträge, die diese Dissertation liefert, sind beispielsweise (i) die Erkenntnis, dass sich der Mittelwert-Varianz-Ansatz zur Bestimmung eines optimalen Portfolios von Vermögensgegenständen auch im Falle einer schiefen Renditeverteilung theoretisch rechtfertigen lässt, (ii) eine umfangreiche Vergleichsstudie mit verschiedenen Ansätzen zur Reduktion der Komplexität von multivariaten Strompreisprognosen und (iii) die Entwicklung eines theoretischen Rahmens und effizienten Algorithmus zur Übersetzung von Preisen für Swing-Optionen in implizite Volatilitäten