Load forecasting using artificial intelligence techniques: a literature survey

Authors:
R. C. Bansal;J. C. Pandey
Affiliations:
Electrical and Electronics Engineering Department, Birla Institute of Technology and Science, Pilani, 333031, Rajasthan, India.;Electrical and Electronics Engineering Department, Birla Institute of Technology and Science, Pilani, 333031, Rajasthan, India
Venue:
International Journal of Computer Applications in Technology
Year:
2005

Citing 11
Cited 4

Single ANN architecture for short-term load forecasting for all seasons

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Intelligent systems for demand forcasting

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KBS and macro-level systems: support of energy demand forecasting

Computers in Industry - Special issue: industrial applications of knowledge-based/expert systems
Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions

Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Soft computing based techniques for short-term load forecasting

Fuzzy Sets and Systems - Clustering and modeling
Forecasting Demand for Electric Power

Advances in Neural Information Processing Systems 5, [NIPS Conference]
A load curve based fuzzy modeling technique for short-term load forecasting

Fuzzy Sets and Systems - Theme: Modeling and learning
Effect of probabilistic inputs on neural network-based electric load forecasting

IEEE Transactions on Neural Networks
ANNSTLF-a neural-network-based electric load forecasting system

IEEE Transactions on Neural Networks
Neural modeling for time series: A statistical stepwise method for weight elimination

IEEE Transactions on Neural Networks

Prediction of corporate financial health by Artificial Neural Network

International Journal of Electronic Finance
Forecasting daily electricity load curves

NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Direct Nyquist array design of PID controllers for boiler-turbine units based on gain and phase margins

International Journal of Computer Applications in Technology
A comparative analysis of power demand forecasting with artificial intelligence and traditional approach

International Journal of Business Information Systems

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Abstract

The forecasting of electricity demand has become one of the major research fields in electrical engineering. In recent years, much research has been carried out on the application of artificial intelligence techniques to the load-forecasting problem. Various artificial intelligence (AI) techniques used for load forecasting are expert systems, fuzzy, genetic algorithm, artificial neural network (ANN), etc. This paper presents an extensive bibliography of more than 265 papers on load forecasting during over past 25 years.