Short-Term Load Forecasting by Artificial Intelligent Technologies

doi:10.3390/books978-3-03897-583-0

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Short-Term Load Forecasting by Artificial Intelligent Technologies

Edited by

January 2019

444 pages

ISBN978-3-03897-582-3 (Paperback)
ISBN978-3-03897-583-0 (PDF)

https://doi.org/10.3390/books978-3-03897-583-0

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This book is a reprint of the Special Issue Short-Term Load Forecasting by Artificial Intelligent Technologies that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Physical Sciences

Summary

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.

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Keywords

artificial intelligence; convolutional neural network; deep neural networks; short-term load forecasting; building energy consumption prediction; deep belief network; contrastive divergence algorithm; least squares learning; energy-consuming pattern; support vector regression; tent chaotic mapping function; cuckoo search algorithm; seasonal mechanism; load forecasting; short-term load forecasting; artificial intelligence; gated recurrent unit; recurrent neural network; power grid; electric vehicle (EV) charging station; short-term load forecasting; niche immunity (NI); lion algorithm (LA); convolutional neural network (CNN); short-term load forecasting; factor analysis; ant colony clustering; extreme learning machine; bat algorithm; short-term load forecasting; electric bus charging station; fuzzy clustering; least squares support vector machine; wolf pack algorithm; short-term load forecasting; interval prediction; lower upper bound estimation; artificial intelligence; multi-objective optimization algorithm; data preprocessing; time series forecasting; ensemble learning; heterogeneous models; SMLE; oil consumption; district heating; load forecasting; machine learning; weather data; consumer behavior; neural networks; support vector machines; clustering; forecasting; hierarchical time-series; individual electrical consumers; scalable; short term; smart meters; wavelets; uncertainty analysis; load forecasting; the Monte Carlo Method (MCM); the Support Vector Machine (SVM) model; hybrid power system; fuel cell; solar; wind; hydrogen; optimization; cost; reliability; short term load forecasting; artificial neural networks; deep learning; natural gas; Electricity Markets; load forecasting models; regression trees; ensemble methods; direct market consumers; short-term load forecasting (STLF); neural networks; artificial intelligence (AI); least squares support vector regression (LS-SVR); chaos theory; quantum computing mechanism (QCM); fruit fly optimization algorithm (FOA); microgrid electric load forecasting (MEL); hybrid forecast model; electrical load forecasting; time series analysis; random forest; multilayer perceptron; deep learning; empirical mode decomposition (EMD); long short-term memory (LSTM); load forecasting; neural networks; variational mode decomposition (VMD); weekly decomposition; India; TEC; short-term; forecasting; black box; hybrid power system; fuel cell; solar; wind; fuel cell; optimization; cost; reliability; artificial neural network; load prediction; smart grid; heuristic optimization; energy trade; accuracy