The energy industry has been going through a significant modernization process over the last decade. Its infrastructure is being upgraded rapidly. The supply, demand and prices are becoming more ...volatile and less predictable than ever before. Even its business model is being challenged fundamentally. In this competitive and dynamic environment, many decision-making processes rely on probabilistic forecasts to quantify the uncertain future. Although most of the papers in the energy forecasting literature focus on point or single-valued forecasts, the research interest in probabilistic energy forecasting research has taken off rapidly in recent years. In this paper, we summarize the recent research progress on probabilistic energy forecasting. A major portion of the paper is devoted to introducing the Global Energy Forecasting Competition 2014 (GEFCom2014), a probabilistic energy forecasting competition with four tracks on load, price, wind and solar forecasting, which attracted 581 participants from 61 countries. We conclude the paper with 12 predictions for the next decade of energy forecasting.
Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the ...historical average. Arguing that model uncertainty and instability seriously impair the forecasting ability of individual predictive regression models, we recommend combining individual forecasts. Combining delivers statistically and economically significant out-of-sample gains relative to the historical average consistently over time. We provide two empirical explanations for the benefits of forecast combination: (i) combining forecasts incorporates information from numerous economic variables while substantially reducing forecast volatility; (ii) combination forecasts are linked to the real economy.
The aim of this paper is to assess whether modeling structural change can help improving the accuracy of macroeconomic forecasts. We conduct a simulated real-time out-of-sample exercise using a ...time-varying coefficients vector autoregression (VAR) with stochastic volatility to predict the inflation rate, unemployment rate and interest rate in the USA. The model generates accurate predictions for the three variables. In particular, the forecasts of inflation are much more accurate than those obtained with any other competing model, including fixed coefficients VARs, time-varying autoregressions and the naïve random walk model. The results hold true also after the mid 1980s, a period in which forecasting inflation was particularly hard.
In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the ...minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on).
Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems.
Forecasting has been an essential part of the power and energy industry. Researchers and practitioners have contributed thousands of papers on forecasting electricity demand and prices, and renewable ...generation (e.g., wind and solar power). This article offers a brief review of influential energy forecasting papers; summarizes research trends; discusses importance of reproducible research and points out six valuable open data sources; makes recommendations about publishing high-quality research papers; and offers an outlook into the future of energy forecasting.
The Planet in 2050 Jäger, Jill; Cornell, Sarah
2011, 20120726, 2012, 2010, 2012-07-26, Letnik:
10
eBook
In 2050, the billions of people living on Earth have found a way to manage the planetary system effectively. Everyone has access to adequate food, shelter, and clean water. Human health is no longer ...considered outside of the health of the ecosystems in which people live. Ecological awareness is an integral part of education. People respond effectively to social and environmental hazards, and societies care for the most vulnerable amongst them. The economy, too, has shifted. Carbon dioxide management is under control, and energy efficiency is the norm. The remaining rainforests have been preserved. Coral reefs are recovering. Fish stocks are thriving. Is any of this really possible? How can our complex social and economic systems interact with a complex planetary system undergoing rapid change to create a future we all want?
This book is a contextualised collation of ideas articulated by the 50 participants of the Planet 2050 workshop held in Lund in October 2008, as part of The Planet in 2050, an interdisciplinary Fast Track Initiative of the International Geosphere-Biosphere Programme. Participants were selected from academia and the sustainability practice community to give a wide-ranging, multi-cultural, trans-disciplinary set of perspectives.
This collection explores four broad sectoral themes: energy and technologies; development, economies and culture; environment; and land use change. By doing so, this book emphasises the importance of a social dialogue on our collective future, and our responsibility to the Earth. It makes strong statements about what needs to happen to the global economy for a sustainable future and documents a new kind of scholarly discussion, engaging people from diverse knowledge communities in a spirit of exploration and reflexivity. The book provides a focus for dialogue and further study for postgraduates and researchers interested in global change as a multi-faceted, socio-environmental
Using a standard decomposition of forecast errors into common and idiosyncratic shocks, we show that aggregate forecast uncertainty can be expressed as the disagreement among the forecasters plus the ...perceived variability of future aggregate shocks. Thus the reliability of disagreement as a proxy for uncertainty will be determined by the stability of the forecasting environment and the length of the forecast horizon. Using density forecasts from the Survey of Professional Forecasters, we find direct evidence in support of our hypothesis. Our results support the use of GARCH-type models, rather than the ex post squared errors in consensus forecasts, to estimate the ex ante variability of aggregate shocks as a component of aggregate uncertainty.
This reprint explores the latest developments and advancements in the application of artificial intelligence (AI) and machine learning (ML) for forecasting and optimization in the field of power ...engineering. In recent years, AI and ML methods have been gaining significant traction and are becoming two of the most important fields in computing. These methods have proven to be effective in solving forecasting and optimization problems in power engineering. The topics covered in the chapters fall into four categories: electricity demand forecasting, wind power forecasting, photovoltaic power forecasting, and optimization.
•The literature on supply chain forecasting is critically reviewed.•The process of involving the forecasting community towards that task is described.•Gaps between theory and practice are ...identified.•Data and software related issues are explicitly considered.•Challenges are summarized followed by suggestions for further research.
Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products).
This paper provides a comprehensive review of the literature and aims at bridging the gap between theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper.
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