Green computing: a realistic evaluation of energy consumption for building load forecasting computation

Aim: The methodology proposed in this paper aims at analyzing the energy consumption, electricity costs, computation time, and accuracy associated with each forecasting algorithm and approach. Furthermore, a monitoring infrastructure is considered to provide inputs to the forecasting approach. Methods: The main objective is to discuss to what extent it is reasonable to increase the consumption of the forecasting approach computation and monitoring infrastructure to achieve more accurate forecasts. Artificial neural networks are used as examples to illustrate the proposed methodology in a building equipped with electricity consumption and other parameters monitoring infrastructure. Results: It has been shown that collecting many parameters and using very accurate forecasting approaches may cause an energy consumption higher than the energy consumption deviation resulting from the forecasting approach with lower accuracy. Conclusion: Finally, it has been shown that green computing, or green computation, requires considering the computation of data, the impact of collecting such data, and the need to perform highly consuming computation tasks.