✦ LIBER ✦

Short-term electric load forecasting using an artificial neural network: case of Northern Vietnam

✍ Scribed by Subhes C. Bhattacharyya; Le Tien Thanh

Publisher: John Wiley and Sons
Year: 2004
Tongue: English
Weight: 131 KB
Volume: 28
Category: Article
ISSN: 0363-907X
DOI: 10.1002/er.980

No coin nor oath required. For personal study only.

✦ Synopsis

Short-term electric load forecasting is an important requirement for electric system operation. This paper employs a feed-forward neural network with a back-propagation algorithm for three types of short-term electric load forecasting: daily peak (valley) load, hourly load and the total load. The forecast has been made for the northern areas of Vietnam using a large set of data on peak load, valley load, hourly load and temperature. The data were used to train and calibrate the artificial neural network, and the calibrated network was used for load forecasting. The results obtained from the model show that the application of neural network to short-term electric load forecasting problem is very useful with quite accurate results. These results compare well with other similar studies.

📜 SIMILAR VOLUMES

Short-term inflow forecasting using an a

Short-term inflow forecasting using an artificial neural network model

✍ Z. X. Xu; J. Y. Li 📂 Article 📅 2002 🏛 John Wiley and Sons 🌐 English ⚖ 230 KB 👁 1 views

## Abstract The primary objective of this study is to investigate the possibility of including more temporal and spatial information on short‐term inflow forecasting, which is not easily attained in the traditional time‐series models or conceptual hydrological models. In order to achieve this objec

Short-term electric power load forecasti

Short-term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

✍ Nicolaos B. Karayiannis; Mahesh Balasubramanian; Heidar A. Malki 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 284 KB 👁 1 views

This article presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks ~FFNNs! and cosine radial basis function ~RBF! neural networks to predict future power demand based on past pow