Akademik Veri Yönetim Sistemi
Proceedings of the Institution of Civil Engineers - Engineering Sustainability, cilt.175, sa.3, ss.154-164, 2022 (SCI-Expanded)
The importance of accurate energy demand modelling has increased to support the decision making of policymakers for ensuring a safe energy supply. However, forecasting energy demand has several difficulties due to the complexity of the supply line, demand increase, non-linearity of data and volatility of energy usage. In this study, an improved grey Verhulst model with a constant term (GVMCT), which is based on the grey model, is introduced for improving the accuracy of energy demand prediction models. Within this context, the total residential electricity demand of both the USA and Turkey is modelled by way of linear and quadratic trend models, as well as three grey models, including the proposed GVMCT model. The effectiveness of the models is assessed based on the mean absolute error (MAE), mean squared error and root mean square error. The results show that the linear trend is the best-performing model, with an MAE of 34 564.81844, for the US data, whereas the proposed GVMCT, with an MAE of 4130.086917, outperforms all models for the data of Turkey.