City Water Demand Forecasting Based on Improved BP Neural Network
| Autor: | Ying Xing, Zhenwei You, Bo Zhang, Xiaoguang Zhou, Ludi Wang, Mengke Yang |
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| Rok vydání: | 2017 |
| Předmět: |
Mathematical optimization
Environmental Engineering Artificial neural network Computer science business.industry Heuristic (computer science) 0208 environmental biotechnology Training time Particle swarm optimization 02 engineering and technology Execution time 020801 environmental engineering Water demand Genetic algorithm Environmental Chemistry Artificial intelligence Municipal planning business Waste Management and Disposal |
| Zdroj: | Journal of Residuals Science and Technology. 14:S111-S117 |
| ISSN: | 2376-578X |
| DOI: | 10.12783/issn.1544-8053/14/s1/15 |
| Popis: | City water demand forecasting is of great significance in reducing the cost of electricity consumption and municipal planning. Back-propagation (BP) neural network has been widely adopted in water demand forecasting in recent years. But BP performs unsatisfactorily in terms of training time and global searching ability, so in this paper we improve BP by two heuristic algorithms, namely, genetic algorithm (GA) and particle swarm optimization (PSO), respectively. The testing and verification of the three algorithms (BP, GA+BP, PSO+BP) have been conducted on real-life water demand forecasting of Beijing city. The obtained results demonstrate that, in spite of the execution time consumed, both GA+BP and PSO+BP performed with higher accuracy and less errors than BP. The obtained results also illustrate that PSO+BP slightly outperformed GA+BP in terms of forecasting accuracy. |
| Databáze: | OpenAIRE |
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