Yes, it is possible to build energy supply model of a country using artificial neural network (ANN). Depending on the architecture, ANN may be classified in a number of different ways which is intimately linked with the learning algorithm used to train the network. Recurrent neural network is one of it. It attempts to incrementally build the autocorrelation structure of a series into the model internally, using feedback connections relying solely on the current values of the input(s) provided externally. Indeed, based on the kinds of time-series problems; the architectural layout of recurrent network takes many different forms. Therefore, you should first identify the proper architecture to build the model.

The answer is yes. Lots of researchers have done that using various AI techniques including ANNs, GA, PSO, etc. I refer you to one of the papers we published on this subject.. Full text can be obtained from the link below. More can be found by searching through Internet . 

M. Yousefi, M. Omid, Sh. Rafiee, S.F. Ghaderi. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN.

Article Strategic planning for minimizing CO2 emissions using LP mod...

Yes, it is possible to build energy supply model of a country using artificial neural network (ANN). Depending on the architecture, ANN may be classified in a number of different ways which is intimately linked with the learning algorithm used to train the network. Recurrent neural network is one of it. It attempts to incrementally build the autocorrelation structure of a series into the model internally, using feedback connections relying solely on the current values of the input(s) provided externally. Indeed, based on the kinds of time-series problems; the architectural layout of recurrent network takes many different forms. Therefore, you should first identify the proper architecture to build the model.

yes it can be used.

only issue is that make sure your data is clean as much as possible.

you can use ANN to learn the different aspects of energy requirements in the different pockets of a country. You can forecast the energy demands of next months and be prepared to meet the requirements. Like you can built a fault tolerant system for a country.

Thank you very much.

All the best

Best regards

Indrajit Mandal

Of course. Generally ANN are applied for solving the task of classification, prediction and image recogntion. The problem is the input and output data representation.

Regards, 

Iakov Korovin

Agree with many answers posted above. It's possible to use NN for this. For more details or how it can be done, refer few of my publications in researchgate.

I wondered WHO down-vote this topic systematically! Either he/she does not know the question, ANN, or doe not understand the answers. We can help if he/she say something.

It is possible but the modelling will need to be complex and dynamic in nature.

Dear @Baraka, Please find below some recent publications dealing with energy forecasting using ANN and other AI techniques (ANFIS, GA, PSO, ...) :

A. Azadeh, S.F. Ghaderi, S. Sohrabkhani, A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran, Energy Policy, 36 (2008) 2637-2644.

L. Ekonomou, Greek long-term energy consumption prediction using artificial neural networks, Energy, 35 (2010) 512-517.

A. Azadeh, S.M. Asadzadeh, A. Ghanbari, An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments, Energy Policy, 38 (2010) 1529-1536.

E. Assareh, M.A. Behrang, A. Ghanbarzdeh, Forecasting Energy Demand in Iran Using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Methods, Energy Sources, Part B: Economics, Planning, and Policy, 7 (2012) 411-422.

Yes, it's possible. according to my experience, your data and their characteristics are very important when you want to extend a new and practical model. As Dr. Omid refered you to my paper, I could not apply ANN because my data were time series and they were not enough for training the neurons in different architectures.