Multi-step forecasting for big data time series based on ensemble learning
A Galicia, R Talavera-Llames, A Troncoso… - Knowledge-Based …, 2019 - Elsevier
This paper presents ensemble models for forecasting big data time series. An ensemble
composed of three methods (decision tree, gradient boosted trees and random forest) is …
composed of three methods (decision tree, gradient boosted trees and random forest) is …
Big data mining of energy time series for behavioral analytics and energy consumption forecasting
Responsible, efficient and environmentally aware energy consumption behavior is
becoming a necessity for the reliable modern electricity grid. In this paper, we present an …
becoming a necessity for the reliable modern electricity grid. In this paper, we present an …
Building energy consumption prediction: An extreme deep learning approach
Building energy consumption prediction plays an important role in improving the energy
utilization rate through helping building managers to make better decisions. However, as a …
utilization rate through helping building managers to make better decisions. However, as a …
Stacking ensemble learning for short-term electricity consumption forecasting
The ability to predict short-term electric energy demand would provide several benefits, both
at the economic and environmental level. For example, it would allow for an efficient use of …
at the economic and environmental level. For example, it would allow for an efficient use of …
Data driven parallel prediction of building energy consumption using generative adversarial nets
C Tian, C Li, G Zhang, Y Lv - Energy and Buildings, 2019 - Elsevier
Building energy consumption prediction is becoming increasingly vital for energy
management, equipment efficiency improvement, cooperation between building energy and …
management, equipment efficiency improvement, cooperation between building energy and …
Predicting energy cost of public buildings by artificial neural networks, CART, and random forest
M Zekić-Sušac, A Has, M Knežević - Neurocomputing, 2021 - Elsevier
The paper deals with modeling the cost of energy consumed in public buildings by
leveraging three machine learning methods: artificial neural networks, CART, and random …
leveraging three machine learning methods: artificial neural networks, CART, and random …
A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
Z Pu, J Yan, L Chen, Z Li, W Tian, T Tao… - Frontiers of Environmental …, 2023 - Springer
Short-term water demand forecasting provides guidance on real-time water allocation in the
water supply network, which help water utilities reduce energy cost and avoid potential …
water supply network, which help water utilities reduce energy cost and avoid potential …
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 …
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 …
A scalable approach based on deep learning for big data time series forecasting
JF Torres, A Galicia, A Troncoso… - Integrated Computer …, 2018 - content.iospress.com
This paper presents a method based on deep learning to deal with big data times series
forecasting. The deep feed forward neural network provided by the H2O big data analysis …
forecasting. The deep feed forward neural network provided by the H2O big data analysis …