Residential energy demand forecasting
External reference persons Claudia De Vizia (firstname.lastname@example.org)
Thesis type SPERIMENTALE
Description Energy demand forecasting is needed for the correct operation and the optimisation of the power system since it allows to plan activities such as energy resources management and storage. Various methods have been analysed for energy demand forecasting (Kalman Filter, regressive analysis and Artificial Neural Network,..), but there is always a need to improve the prediction.
Moreover, the growth in electricity consumption over the last decade is due to the residential and services sectors. Thus, residential energy demand forecasting is taking on an increasingly important role and ad-hoc methods may be considered.
This thesis aims at improving the household load forecasting comparing different methods and taking into account different parameters.
Indeed, household energy consumption is strictly related to the userís habit. Thus, including usersí behaviour and usersí preference parameters would increase the accuracy of the load forecasting.
Deadline 07/08/2021 PROPONI LA TUA CANDIDATURA