Perpustakaan Digital ITB

The capability to forecast how differences in patterns of utilization in various kinds of loads can influence energy usage is an essential effort to decrease carbon emissions and demand-side energy management. The difference in weather change starts as the first step to change the energy consumption pattern in the domestic, commercial and industrial sector. To find the change in climate and their impact on energy usage, this study examines the medium-term (MT) and long-term (LT) energy prediction for utilities, independent power producers and industrial customers to estimate the energy usage requirement of large-scale city-wide by means of using the nonlinear ACCEPTED MANUSCRIPT autoregressive model (NARM), linear model using stepwise regression (LMSR) and random forest (least square boosting) (LSBoost) approaches, based on actual environmental as well as energy consumption data. The irregular load pattern recognition to remove the abnormal trend in actual energy usage is performed by applying the outlier detection and clustering analysis. The coefficient of variation (CV) of LSBoost model is 5.019%, 3.159%, 3.292% and 3.184% in summer, autumn, winter and spring season respectively. The machine learning (ML) techniques are validated and compared based on performance and accuracy with the previously existing Gaussian process regression (GPR) model. The optimal modeling of city-wide energy demand prediction using ML-based models are guaranteed the accurate operation and design of distributed energy systems