There is a growing demand for Green AI (Artificial Intelligence) technologies in the market
and society, as it emerges as a promising technology. Green AI technologies are used to …

Abstract Time Series Extrinsic Regression (TSER) involves using a set of training time series
to form a predictive model of a continuous response variable that is not directly related to the …

Predicting electricity consumption is not an easy task depending on many factors that affect
energy consumption. Therefore, electricity utilities and governments are always searching …

Artificial intelligence and machine learning applications are of significant importance almost
in every field of human life to solve problems or support human experts. However, the …

Electric consumption prediction methods are investigated for many reasons, such as
decision-making related to energy efficiency as well as for anticipating demand and the …

Multivariate chaotic time series prediction is a challenging task, especially when multiple
variables are predicted simultaneously. For multiple related prediction tasks typically require …

Research in energy prediction is widely explored as it is used in long term planning like
development investment and resource planning to estimating tariffs and analyzing and …

This research focuses on the crucial task of accurately forecasting electricity consumption, a
key concern in modern societies where electricity is essential for industries, healthcare, and …

Since the rapid development of Internet of Things (IoT) has promoted the dramatic growth of
data, data aggregation has received considerable attention, which can collect the sensed …

Deep state-space models (DSSMs) have gained popularity in recent years due to their
potent modeling capacity for dynamic systems. However, existing DSSM works are limited to …