Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
[HTML][HTML] Electricity price forecasting: A review of the state-of-the-art with a look into the future
R Weron - International journal of forecasting, 2014 - Elsevier
A variety of methods and ideas have been tried for electricity price forecasting (EPF) over the
last 15 years, with varying degrees of success. This review article aims to explain the …
last 15 years, with varying degrees of success. This review article aims to explain the …
Rolling element bearing fault diagnosis using convolutional neural network and vibration image
DT Hoang, HJ Kang - Cognitive Systems Research, 2019 - Elsevier
Detecting in prior bearing faults is an essential task of machine health monitoring because
bearings are the vital components of rotary machines. The performance of traditional …
bearings are the vital components of rotary machines. The performance of traditional …
Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods
Z Yang, L Ce, L Lian - Applied Energy, 2017 - Elsevier
Electricity prices have rather complex features such as high volatility, high frequency,
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
nonlinearity, mean reversion and non-stationarity that make forecasting very difficult …
Electricity price forecasting using recurrent neural networks
Accurate electricity price forecasting has become a substantial requirement since the
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
liberalization of the electricity markets. Due to the challenging nature of electricity prices …
On the use of cross-validation for time series predictor evaluation
In time series predictor evaluation, we observe that with respect to the model selection
procedure there is a gap between evaluation of traditional forecasting procedures, on the …
procedure there is a gap between evaluation of traditional forecasting procedures, on the …
Forecast the electricity price of US using a wavelet transform-based hybrid model
W Qiao, Z Yang - Energy, 2020 - Elsevier
Wavelet transform (WT), as a data preprocessing algorithm, has been widely applied in
electricity price forecasting. However, this deterministic-based algorithm does not present …
electricity price forecasting. However, this deterministic-based algorithm does not present …
Day-ahead electricity price forecasting via the application of artificial neural network based models
Traditionally, short-term electricity price forecasting has been essential for utilities and
generation companies. However, the deregulation of electricity markets created a …
generation companies. However, the deregulation of electricity markets created a …
A hybrid approach of adaptive wavelet transform, long short-term memory and ARIMA-GARCH family models for the stock index prediction
M Zolfaghari, S Gholami - Expert Systems with Applications, 2021 - Elsevier
Modelling and forecasting the stock price constitute an important area of financial research
for both academics and practitioners. This study seeks to determine whether improvements …
for both academics and practitioners. This study seeks to determine whether improvements …
Dense skip attention based deep learning for day-ahead electricity price forecasting
The forecasting of the day-ahead electricity price (DAEP) has become more of interest to
decision makers in the liberalized market, as it can help optimize bidding strategies and …
decision makers in the liberalized market, as it can help optimize bidding strategies and …