An adaptive neural network approach to one-week ahead load forecasting

Journal Article · Sun Aug 01 04:00:00 EDT 1993 · IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/59.260877· OSTI ID:5616981

Peng, T M ^[1]; Hubele, N F; Karady, G G ^[2]

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1. Pacific Gas and Electric Co., San Francisco, CA (United States)
2. Arizona State Univ., Tempe, AZ (United States)

A new neural network approach is applied to one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline.'' An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. Load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized Least Mean Square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4 percent are derived from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts.

• OSTI ID: 5616981
• Journal Information: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States), Journal Name: IEEE Transactions on Power Systems (Institute of Electrical and Electronics Engineers); (United States) Vol. 8:3; ISSN 0885-8950; ISSN ITPSEG
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
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29 ENERGY PLANNING, POLICY, AND ECONOMY
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296000 -- Energy Planning & Policy-- Electric Power
COMPUTERIZED SIMULATION
DEMAND
ENERGY SYSTEMS
FORECASTING
LEARNING
LOAD ANALYSIS
NEURAL NETWORKS
POWER DEMAND
POWER SYSTEMS
SIMULATION