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This paper presents an analysis of the retrofitting process of an urban area. The predictions for the energy consumption was made using a series of software programs created using artificial neural networks instead of simulation software or standard computing or numerical methods. The novelty of this method is that energy consumption was determined based on real data collected from numerous real cases instead of standard old norms, leading to a more accurate prediction. This method takes into consideration the nonlinearity relations between all the measurable variables and the final energy consumption value, without being restricted to standards and norms. The construction of the neural networks was presented in previous papers. The main goal of the analysis was to determine the most efficient methods for the retrofitting of the residential buildings and the economic costs.

Energy Consumption Prediction Analysis for the Retrofitting of an Urban Area using Artificial Neural Networks