Recurrent Neural Networks for Short-Term Load Forecasting
Recurrent Neural Networks for Short-Term Load Forecasting

Author: Filippo Maria Bianchi

Publisher: Springer

Published: 2017-11-09

Total Pages: 74

ISBN-13: 3319703382

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The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

Short Term Forecasting of Cloud and Precipitation
Short Term Forecasting of Cloud and Precipitation

Author: Alan R. Bohne

Publisher:

Published: 1988

Total Pages: 106

ISBN-13:

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A methodology for real-time operations has been developed for the short-term forecasting of cloud and precipitation fields. Pattern recognition techniques are employed to extract useful features from the data field and extrapolation techniques are used to project these features into the future. To reduce computational load, contours defined by directional codes are used to delineate features. These contours are subdivided and attributes such as length, location, and location of each segment are determined. Segment matching is performed for successive observations and attribute changes are monitored over time. Several techniques for the forecasting of attributes have been explored, and an exponential smoothing filter and a linear trend adaptive smoothing filter have been chosen as most appropriate. Currently analysis is performed on a minicomputer and image processor system utilizing radar reflectivity data. Refinement of these techniques and extension into a more comprehensive short term forecasting program is planned.

Forecasting: principles and practice
Forecasting: principles and practice

Author: Rob J Hyndman

Publisher: OTexts

Published: 2018-05-08

Total Pages: 380

ISBN-13: 0987507117

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

How Accurate Are the Imf's Short-Term Forecasts? Another Examination of the World Economic Outlook
How Accurate Are the Imf's Short-Term Forecasts? Another Examination of the World Economic Outlook

Author: Mr.Michael J. Artis

Publisher: International Monetary Fund

Published: 1996-08-01

Total Pages: 94

ISBN-13: 1451851251

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This paper analyzes the short-term forecasts for industrial and developing countries produced by the International Monetary Fund, and published twice a year in the World Economic Outlook (WEO). For the industrial country group, the WEO forecasts for output growth and inflation are satisfactory and pass most conventional tests in forecasting economic developments, although forecast accuracy has not improved over time, and predicting the turning points of the business cycle remains a weakness. For the developing countries, the task of forecasting movements in economic activity is even more difficult and the conventional measures of forecast accuracy are less satisfactory than for the industrial countries.

Short-Term Load Forecasting 2019
Short-Term Load Forecasting 2019

Author: Antonio Gabaldón

Publisher: MDPI

Published: 2021-02-26

Total Pages: 324

ISBN-13: 303943442X

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Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.