[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review

Y Chen, M Guo, Z Chen, Z Chen, Y Ji - Energy Reports, 2022 - Elsevier
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …

Completed review of various solar power forecasting techniques considering different viewpoints

YK Wu, CL Huang, QT Phan, YY Li - Energies, 2022 - mdpi.com
Solar power has rapidly become an increasingly important energy source in many countries
over recent years; however, the intermittent nature of photovoltaic (PV) power generation …

Fastest‐growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

W Qiao, Z Li, W Liu, E Liu - International Journal of Energy …, 2022 - Wiley Online Library
Electricity is an important indicator for economic development, especially electricity
production (EP), which is electricity industry managers making strategic decisions. There are …

Forecasting crude oil futures prices using BiLSTM-Attention-CNN model with Wavelet transform

Y Lin, K Chen, X Zhang, B Tan, Q Lu - Applied Soft Computing, 2022 - Elsevier
In this study, a novel hybrid model for forecasting crude oil futures price time series is
proposed. The combination of Bidirectional long short-term memory network (BiLSTM) …

Time series forecasting using ensemble learning methods for emergency prevention in hydroelectric power plants with dam

SF Stefenon, MHDM Ribeiro, A Nied, KC Yow… - Electric Power Systems …, 2022 - Elsevier
In hydroelectric plants, the responsibility for the operation of the reservoirs typically lies with
the national system operator, who controls the level of the reservoirs based on a stochastic …

LOWESS smoothing and Random Forest based GRU model: A short-term photovoltaic power generation forecasting method

Y Dai, Y Wang, M Leng, X Yang, Q Zhou - Energy, 2022 - Elsevier
Accurate prediction of photovoltaic power generation is vital to guarantee smooth operation
of power stations and ensure users' electricity consumption. As a good forecasting tool …

[HTML][HTML] Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory

Y Chen, D Zhang - Advances in Applied Energy, 2021 - Elsevier
Electricity constitutes an indispensable source of secondary energy in modern society.
Accurate and robust short-term electrical load forecasting is essential for more effective …

A review of behind-the-meter solar forecasting

BC Erdener, C Feng, K Doubleday, A Florita… - … and Sustainable Energy …, 2022 - Elsevier
Solar photovoltaic systems largely integrated within the distribution grid are operated
'behind-the-meter'and power generation cannot be directly monitored by most utilities. The …

Short-term load forecasting for microgrid energy management system using hybrid SPM-LSTM

A Jahani, K Zare, LM Khanli - Sustainable Cities and Society, 2023 - Elsevier
Load forecasting in power microgrids and load management systems is still a challenge and
needs an accurate method. Although in recent years, short-term load forecasting is done by …

[HTML][HTML] Net-load forecasting of renewable energy systems using multi-input LSTM fuzzy and discrete wavelet transform

MJ Mokarram, R Rashiditabar, M Gitizadeh, J Aghaei - Energy, 2023 - Elsevier
This paper represents a new framework to forecast electricity power net-load in renewable
energy systems. Estimating electricity power net-load with high accuracy affects economic …