Automated Recurrent Neural Network Design of a Neural Controller in a Custom Power Device
Journal of Intelligent and Robotic Systems
Computational Intelligence Techniques for Short-Term Electric Load Forecasting
Journal of Intelligent and Robotic Systems
Load forecasting using artificial intelligence techniques: a literature survey
International Journal of Computer Applications in Technology
Stream-Based Electricity Load Forecast
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Artificial Immune System for Short-Term Electric Load Forecasting
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A system for analysis and prediction of electricity-load streams
Intelligent Data Analysis - Knowledge Discovery from Data Streams
Forecasting Portugal global load with artificial neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Using genetic algorithm to develop a neural-network-based load forecasting
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Engineering Applications of Artificial Intelligence
Electricity load forecasting using self organizing maps
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
A new SOM algorithm for electricity load forecasting
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Neural network based modelling of environmental variables: A systematic approach
Mathematical and Computer Modelling: An International Journal
Simulating the impact of building occupant peer networks on inter-building energy consumption
Proceedings of the Winter Simulation Conference
Proceedings of the Industrial Track of the 13th ACM/IFIP/USENIX International Middleware Conference
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A key component of the daily operation and planning activities of an electric utility is short-term load forecasting, i.e., the prediction of hourly loads (demand) for the next hour to several days out. The accuracy of such forecasts has significant economic impact for the utility. This paper describes a load forecasting system known as ANNSTLF (artificial neural-network short-term load forecaster) which has received wide acceptance by the electric utility industry and presently is being used by 32 utilities across the USA and Canada. ANNSTLF can consider the effect of temperature and relative humidity on the load. Besides its load forecasting engine, ANNSTLF contains forecasters that can generate the hourly temperature and relative humidity forecasts needed by the system. ANNSTLF is based on a multiple ANN strategy that captures various trends in the data. Both the first and the second generation of the load forecasting engine are discussed and compared. The building block of the forecasters is a multilayer perceptron trained with the error backpropagation learning rule. An adaptive scheme is employed to adjust the ANN weights during online forecasting. The forecasting models are site independent and only the number of hidden layer nodes of ANN's need to be adjusted for a new database. The results of testing the system on data from ten different utilities are reported