[HTML][HTML] Forecasting: theory and practice
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
uncertainty that surrounds the future is both exciting and challenging, with individuals and …
Data analytics in the supply chain management: Review of machine learning applications in demand forecasting
A Aamer, LP Eka Yani… - Operations and Supply …, 2020 - journal.oscm-forum.org
In today's fast-paced global economy coupled with the availability of mobile internet and
social networks, several business models have been disrupted. This disruption brings a …
social networks, several business models have been disrupted. This disruption brings a …
Day-ahead electricity price forecasting via the application of artificial neural network based models
IP Panapakidis, AS Dagoumas - Applied Energy, 2016 - Elsevier
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
Day-ahead load forecast using random forest and expert input selection
The electrical load forecast is getting more and more important in recent years due to the
electricity market deregulation and integration of renewable resources. To overcome the …
electricity market deregulation and integration of renewable resources. To overcome the …
LSTM enhanced by dual-attention-based encoder-decoder for daily peak load forecasting
K Zhu, Y Li, W Mao, F Li, J Yan - Electric Power Systems Research, 2022 - Elsevier
Daily peak load forecasting is a challenging problem in the filed of electric power load
forecasting. Since the nonlinear and dynamic of influence factors and their sequential …
forecasting. Since the nonlinear and dynamic of influence factors and their sequential …
Robust ensemble learning framework for day-ahead forecasting of household based energy consumption
Smart energy management mandates a more decentralized energy infrastructure, entailing
energy consumption information on a local level. Household-based energy consumption …
energy consumption information on a local level. Household-based energy consumption …
A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting
L Xiao, W Shao, T Liang, C Wang - Applied energy, 2016 - Elsevier
Short-term load forecasting (STLF) plays an irreplaceable role in the efficient management
of electric systems. Particularly in the electricity market and industry, accurate forecasting …
of electric systems. Particularly in the electricity market and industry, accurate forecasting …
Mixed kernel based extreme learning machine for electric load forecasting
Short term electric load forecasting, as an important tool in the electricity market, plays a
critical role in the management of electric systems. Proposing an accuracy and optimization …
critical role in the management of electric systems. Proposing an accuracy and optimization …
A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting
The ultimate issue in electricity loads modelling is to improve forecasting accuracy as well as
guarantee a robust prediction result, which will save considerable manual labor material …
guarantee a robust prediction result, which will save considerable manual labor material …
Research and application of a hybrid model based on multi-objective optimization for electrical load forecasting
L Xiao, W Shao, C Wang, K Zhang, H Lu - Applied energy, 2016 - Elsevier
Short-term load forecasting (STLF) plays an important role in the efficient management of
electric systems. Building an optimization model that will enhance forecasting accuracy is …
electric systems. Building an optimization model that will enhance forecasting accuracy is …