Electricity Consumption and Generation Forecasting with Artificial Neural Networks
Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as time-of-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their p...
- Autores:
- Tipo de recurso:
- Book
- Fecha de publicación:
- 2017
- Institución:
- Universidad de Bogotá Jorge Tadeo Lozano
- Repositorio:
- Expeditio: repositorio UTadeo
- Idioma:
- eng
- OAI Identifier:
- oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16829
- Acceso en línea:
- https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/electricity-consumption-and-generation-forecasting-with-artificial-neural-networks
http://hdl.handle.net/20.500.12010/16829
- Palabra clave:
- Ingeniería
Redes neuronales artificiales
Energía renovable
Medidores inteligentes
- Rights
- License
- Abierto (Texto Completo)
| Summary: | Nowadays, smart meters, sensors and advanced electricity tariff mechanisms such as time-of-use tariff (ToUT), critical peak pricing tariff and real time tariff enable the electricity consumption optimization for residential consumers. Therefore, consumers will play an active role by shifting their peak consumption and change dynamically their behavior by scheduling home appliances, invest in small generation or storage devices (such as small wind turbines, photovoltaic (PV) panels and electrical vehicles). Thus, the current load profile curves for household consumers will become obsolete and electricity suppliers will require dynamical load profiles calculation and new advanced methods for consumption forecast. In this chapter, we aim to present some developments of artificial neural networks for energy demand side management system that determines consumers’ profiles and patterns, consumption forecasting and also small generation estimations |
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