A review on time series forecasting techniques for building energy consumption
Energy consumption forecasting for buildings has immense value in energy efficiency and
sustainability research. Accurate energy forecasting models have numerous implications in …
sustainability research. Accurate energy forecasting models have numerous implications in …
A review on applications of ANN and SVM for building electrical energy consumption forecasting
The rapid development of human population, buildings and technology application currently
has caused electric consumption to grow rapidly. Therefore, efficient energy management …
has caused electric consumption to grow rapidly. Therefore, efficient energy management …
Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders
Energy consumption in buildings is increasing because of social development and
urbanization. Forecasting the energy consumption in buildings is essential for improving …
urbanization. Forecasting the energy consumption in buildings is essential for improving …
Forecasting diurnal cooling energy load for institutional buildings using Artificial Neural Networks
C Deb, LS Eang, J Yang, M Santamouris - Energy and Buildings, 2016 - Elsevier
This study presents a methodology to forecast diurnal cooling load energy consumption for
institutional buildings using data driven techniques. The cases for three institutional …
institutional buildings using data driven techniques. The cases for three institutional …
Physicochemical parameters data assimilation for efficient improvement of water quality index prediction: Comparative assessment of a noise suppression …
Water quality has a crucial impact on human health; therefore, water quality index modeling
is one of the challenging issues in the water sector. The accurate prediction of water quality …
is one of the challenging issues in the water sector. The accurate prediction of water quality …
Prediction of building power consumption using transfer learning-based reference building and simulation dataset
Y Ahn, BS Kim - Energy and Buildings, 2022 - Elsevier
With the advancements in data processing technologies and the increased use of
renewable energy systems, the development of microgrid has gained attention …
renewable energy systems, the development of microgrid has gained attention …
Air temperature forecasting using artificial neural network for Ararat valley
H Astsatryan, H Grigoryan, A Poghosyan… - Earth Science …, 2021 - Springer
The air temperature is a critical factor in many societal challenges to protect human health
and the environment. Moreover, a vital climatic parameter, the temperature has a direct …
and the environment. Moreover, a vital climatic parameter, the temperature has a direct …
Modeling soil temperatures at different depths by using three different neural computing techniques
This study compares the accuracy of three different neural computing techniques, multi-layer
perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural …
perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural …
EMD-Att-LSTM: a data-driven strategy combined with deep learning for short-term load forecasting
Electric load forecasting is an efficient tool for system planning, and consequently, building
sustainable power systems. However, achieving desirable performance is difficult owing to …
sustainable power systems. However, achieving desirable performance is difficult owing to …
Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques
O Kisi, H Sanikhani, M Cobaner - Theoretical and Applied Climatology, 2017 - Springer
The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system
(ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at …
(ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at …