A review of machine learning in building load prediction
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …
opportunities for applying machine learning to building energy system modeling and …
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 …
Metaheuristic design of feedforward neural networks: A review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a
key interest among the researchers and practitioners of multiple disciplines. The FNN …
key interest among the researchers and practitioners of multiple disciplines. The FNN …
Genetic algorithm-optimized multi-channel convolutional neural network for stock market prediction
H Chung, K Shin - Neural Computing and Applications, 2020 - Springer
Recently, artificial intelligence technologies have received considerable attention because
of their practical applications in various fields. The key factor in this prosperity is deep …
of their practical applications in various fields. The key factor in this prosperity is deep …
Forecasting oil production using ensemble empirical model decomposition based Long Short-Term Memory neural network
W Liu, WD Liu, J Gu - Journal of Petroleum Science and Engineering, 2020 - Elsevier
Oil production forecasting is an important means of understanding and effectively
developing reservoirs. Reservoir numerical simulation is the most mature and effective …
developing reservoirs. Reservoir numerical simulation is the most mature and effective …
Review of automated time series forecasting pipelines
Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …
energy systems and economics. Creating a forecasting model for a specific use case …
Comparison of ELM, GANN, WNN and empirical models for estimating reference evapotranspiration in humid region of Southwest China
Y Feng, N Cui, L Zhao, X Hu, D Gong - Journal of Hydrology, 2016 - Elsevier
Reference evapotranspiration (ET 0) is an essential component in hydrological ecological
processes and agricultural water management. Accurate estimation of ET 0 is of importance …
processes and agricultural water management. Accurate estimation of ET 0 is of importance …
Fractional order neural networks for system identification
CJZ Aguilar, JF Gómez-Aguilar… - Chaos, Solitons & …, 2020 - Elsevier
Neural networks and fractional order calculus have shown to be powerful tools for system
identification. In this paper we combine both approaches to propose a fractional order neural …
identification. In this paper we combine both approaches to propose a fractional order neural …
Optimizing the artificial neural network parameters using a biased random key genetic algorithm for time series forecasting
Abstract Artificial Neural Networks (ANN) is one of the most used methods in time series
forecasting. Mostly, it is hard to determine the design and weight parameters of ANNs by …
forecasting. Mostly, it is hard to determine the design and weight parameters of ANNs by …
Temperature Forecasting via Convolutional Recurrent Neural Networks Based on Time‐Series Data
Z Zhang, Y Dong - Complexity, 2020 - Wiley Online Library
Today, artificial intelligence and deep neural networks have been successfully used in many
applications that have fundamentally changed people's lives in many areas. However, very …
applications that have fundamentally changed people's lives in many areas. However, very …