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The paper is concerned with predicting energy consumption in the production and product usage stages and searching for possible changes in product design to reduce energy consumption. The prediction of energy consumption uses parametric models based on regression analysis and artificial neural networks. In turn, simulations related to the identification of improvement opportunities for reducing energy consumption are performed using a constraint programming technique. The results indicate that the use of artificial neural networks improves the quality of an estimation model. Moreover, constraint programming enables the identification of all possible solutions to a constraint satisfaction problem, if there are any. These solutions support R&D specialists in identifying possibilities for reducing energy consumption through changes in product specifications. The proposed approach is dedicated to products related to high-cost energy use, which can be manufactured, for example, by companies belonging to the household appliance industry.
