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Modeling and forecasting building energy consumption: A review of data-driven techniques
Building energy consumption modeling and forecasting is essential to address buildings
energy efficiency problems and take up current challenges of human comfort, urbanization …
energy efficiency problems and take up current challenges of human comfort, urbanization …
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 …
Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms
ME Javanmard, SF Ghaderi - Sustainable Cities and Society, 2023 - Elsevier
With the growth of population, many countries face the challenge of supplying energy
resources. One approach to managing and planning these resources is to predict energy …
resources. One approach to managing and planning these resources is to predict energy …
Hybrid structures in time series modeling and forecasting: A review
The key factor in selecting appropriate forecasting model is accuracy. Given the deficiencies
of single models in processing various patterns and relationships latent in data, hybrid …
of single models in processing various patterns and relationships latent in data, hybrid …
Financial time series forecasting with the deep learning ensemble model
K He, Q Yang, L Ji, J Pan, Y Zou - Mathematics, 2023 - mdpi.com
With the continuous development of financial markets worldwide to tackle rapid changes
such as climate change and global warming, there has been increasing recognition of the …
such as climate change and global warming, there has been increasing recognition of the …
Water level prediction through hybrid SARIMA and ANN models based on time series analysis: Red hills reservoir case study
Reservoir water level (RWL) prediction has become a challenging task due to spatio-
temporal changes in climatic conditions and complicated physical process. The Red Hills …
temporal changes in climatic conditions and complicated physical process. The Red Hills …
Multi-zone indoor temperature prediction with LSTM-based sequence to sequence model
Accurate indoor temperature forecasting can facilitate energy savings of the building without
compromising the occupant comfort level, by providing more accurate control of the HVAC …
compromising the occupant comfort level, by providing more accurate control of the HVAC …
A hybrid model with applying machine learning algorithms and optimization model to forecast greenhouse gas emissions with energy market data
ME Javanmard, SF Ghaderi - Sustainable Cities and Society, 2022 - Elsevier
In recent decades, many countries have encountered air pollution and environmental
problems caused by greenhouse gas (GHG) emissions. One of the essential approaches to …
problems caused by greenhouse gas (GHG) emissions. One of the essential approaches to …
A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting
In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to
inaccurate forecasting of sales which leads to incorrect ordering of products. The time series …
inaccurate forecasting of sales which leads to incorrect ordering of products. The time series …
Improving monthly rainfall forecast in a watershed by combining neural networks and autoregressive models
The main aim of the rain forecast is to determine rain occurrence conditions in a specific
location. This is considered of vital importance to assess the availability of water resources …
location. This is considered of vital importance to assess the availability of water resources …