Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score
Electric power forecasting plays a substantial role in the administration and balance of
current power systems. For this reason, accurate predictions of service demands are needed …
current power systems. For this reason, accurate predictions of service demands are needed …
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study
Forecasting the evolution of complex systems is noted as one of the 10 grand challenges of
modern science. Time series data from complex systems capture the dynamic behaviors and …
modern science. Time series data from complex systems capture the dynamic behaviors and …
[HTML][HTML] A comprehensive survey on load forecasting hybrid models: Navigating the Futuristic demand response patterns through experts and intelligent systems
Load forecasting is a crucial task, which is carried out by utility companies for sake of power
grids' successful planning, optimized operation and control, enhanced performance, and …
grids' successful planning, optimized operation and control, enhanced performance, and …
[HTML][HTML] An electricity load forecasting model for Integrated Energy System based on BiGAN and transfer learning
D Zhou, S Ma, J Hao, D Han, D Huang, S Yan, T Li - Energy Reports, 2020 - Elsevier
Abstract Integrated Energy System (IES) is able to collaborate various energy systems and
boost energy supply efficiency. To further facilitate the energy scheduling in IES, load …
boost energy supply efficiency. To further facilitate the energy scheduling in IES, load …
Monthly electric load forecasting using transfer learning for smart cities
Monthly electric load forecasting is essential to efficiently operate urban power grids.
Although diverse forecasting models based on artificial intelligence techniques have been …
Although diverse forecasting models based on artificial intelligence techniques have been …
Forecasting monthly and quarterly time series using STL decomposition
M Theodosiou - International Journal of Forecasting, 2011 - Elsevier
This paper is a re-examination of the benefits and limitations of decomposition and
combination techniques in the area of forecasting, and also a contribution to the field …
combination techniques in the area of forecasting, and also a contribution to the field …
Monthly electricity consumption forecasting method based on X12 and STL decomposition model in an integrated energy system
T Sun, T Zhang, Y Teng, Z Chen… - … Problems in Engineering, 2019 - Wiley Online Library
With the rapid development and wide application of distributed generation technology and
new energy trading methods, the integrated energy system has developed rapidly in Europe …
new energy trading methods, the integrated energy system has developed rapidly in Europe …
[HTML][HTML] Decomposition forecasting methods: A review of applications in power systems
The aim of this paper is to present a comprehensive literature review on the application of
decomposition methods of time series forecasting in power systems. A comprehensive …
decomposition methods of time series forecasting in power systems. A comprehensive …
A machine learning model ensemble for mixed power load forecasting across multiple time horizons
The increasing penetration of renewable energy sources tends to redirect the power
systems community's interest from the traditional power grid model towards the smart grid …
systems community's interest from the traditional power grid model towards the smart grid …