Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
SK Hora, R Poongodan, RP De Prado, M Wozniak… - Applied Sciences, 2021 - mdpi.com
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
Time-series production forecasting method based on the integration of Bidirectional Gated Recurrent Unit (Bi-GRU) network and Sparrow Search Algorithm (SSA)
X Li, X Ma, F Xiao, C Xiao, F Wang, S Zhang - Journal of Petroleum Science …, 2022 - Elsevier
With the gowning demand of improving quality and benefit of unconventional resources,
time-series production prediction plays an increasingly essential role in economic …
time-series production prediction plays an increasingly essential role in economic …
A comprehensive evaluation of ensemble learning for stock-market prediction
Stock-market prediction using machine-learning technique aims at developing effective and
efficient models that can provide a better and higher rate of prediction accuracy. Numerous …
efficient models that can provide a better and higher rate of prediction accuracy. Numerous …
Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam
In hydroelectric plants, the responsibility for the operation of the reservoirs typically lies with
the national system operator, who controls the level of the reservoirs based on a stochastic …
the national system operator, who controls the level of the reservoirs based on a stochastic …
Electric vehicle energy consumption prediction using stacked generalization: An ensemble learning approach
In this paper, we present an ensemble stacked generalization (ESG) approach for better
prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of …
prediction of electric vehicles (EVs) energy consumption. ESG is a weighted combination of …
Combination of short-term load forecasting models based on a stacking ensemble approach
Building electric energy consumption forecasting is essential in establishing an energy
operation strategy for building energy management systems. Because of recent …
operation strategy for building energy management systems. Because of recent …
An ensemble energy consumption forecasting model based on spatial-temporal clustering analysis in residential buildings
Due to the availability of smart metering infrastructure, high-resolution electric consumption
data is readily available to study the dynamics of residential electric consumption at finely …
data is readily available to study the dynamics of residential electric consumption at finely …
An enhanced ensemble learning-based fault detection and diagnosis for grid-connected PV systems
The main objective of this article is to develop an enhanced ensemble learning (EL) based
intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high …
intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high …
A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings
Smart buildings are equipped with sensors that allow monitoring a range of building systems
including heating and air conditioning, lighting and the general electric energy consumption …
including heating and air conditioning, lighting and the general electric energy consumption …
Stacking Deep learning and Machine learning models for short-term energy consumption forecasting
S Reddy, S Akashdeep, R Harshvardhan… - Advanced Engineering …, 2022 - Elsevier
Accurate prediction of electricity consumption is essential for providing actionable insights to
decision-makers for managing volume and potential trends in future energy consumption for …
decision-makers for managing volume and potential trends in future energy consumption for …