Computational modeling demonstrates short-term electricity load forecasting using an integrated artificial neural network, indicating its utility for power generation scheduling and system security.
The demand for electricity is known to vary by the time of the day, week, month, temperature, and usage habits of the consumers. Though usage habit is not directly observable, it may be implied in the patterns of usage that have occurred in the past. A short-term load-forecasting (STLF) program that uses an integrated artificial neural network (ANN) approach is capable of predicting load for basic generation scheduling functions, assessing power system security, and providing timely dispatcher information. How well training data is chosen in an ANN is the defining factor in how well the network's output will match the event being modeled.
No takes yet. Share an insight, caveat, or question.
Swarup et al. (2002) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: