Cloud-based long term electricity demand forecasting using artificial neuro-fuzzy and neural networks

The supply-demand equilibrium is the main criteria for determination of electricity pricing for both electrical power production companies and ordinary (household) users. The companies must be sure about future demands of electricity for uninterrupted efficient electrical supply. The demand of electricity is affected from weather conditions, process of economy, working and nonworking days of a year, etc. Therefore, forecasting demand by using current and historical data is very important for electricity trading and producing companies. In this study, a cloud-based forecasting service which is based on neural network model is proposed for long-term electricity demand forecasting of Turkey. Cloud based nature of the proposed system help continuous training and improved forecasting capability over time from the system. Following year overall electric demand is approximately estimated with neural network and artificial neuro-fuzzy inference systems.