Name
Title | ||
|---|---|---|
Computational Intelligence Techniques For Home Electric Load Forecasting And Balancing |
Abstract | ||
|---|---|---|
The paper presents an electric load balancing system for domestic use. An electric load forecasting system, which is realized by a genetic algorithm-based modified neural network, is employed. On forecasting the home power consumption profile, the load balancing system can adjust the amount of energy stored in battery accordingly, preventing it from reaching certain practical limits. A steady consumption from the AC mains can then be obtained which will benefit both the users and the utility company. An example will be given to illustrate the merits of the forecaster, and its performance on achieving the load balancing. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1142/S1469026805001659 | INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS |
Keywords | Field | DocType |
Genetic algorithms, load balancing, neural networks, short-term load forecasting | Load balancing (electrical power),Electrical load,Computer science,Real-time computing,Artificial intelligence,Artificial neural network,Genetic algorithm,Computational intelligence,Load balancing (computing),Mains electricity,Battery (electricity),Machine learning,Reliability engineering | Journal |
Volume | Issue | ISSN |
5 | 3 | 1469-0268 |
Citations | PageRank | References |
3 | 0.40 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| S. H. Ling | 1 | 609 | 40.29 |
| F. H. Frank Leung | 2 | 183 | 16.00 |
| L. K. Wong | 3 | 3 | 0.40 |
| H. K. Lam | 4 | 3618 | 193.15 |