[HTML][HTML] Forecasting: theory and practice

F Petropoulos, D Apiletti, V Assimakopoulos… - International Journal of …, 2022 - Elsevier
Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Hybridization of hybrid structures for time series forecasting: A review

Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and develo** a forecasting framework with a high degree of accuracy is one of the most …

Applications of random forest in multivariable response surface for short-term load forecasting

GF Fan, LZ Zhang, M Yu, WC Hong, SQ Dong - International Journal of …, 2022 - Elsevier
Accurate load forecasting is helpful for optimizing the use of power resources. To this end,
this investigation proposes a hybrid model for short-term load forecasting, namely the RF …

A transformer-based method of multienergy load forecasting in integrated energy system

C Wang, Y Wang, Z Ding, T Zheng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Multienergy load forecasting technique is the basis for the operation and scheduling of
integrated energy system. Different types of loads in an integrated energy system, ie …

PM2. 5 volatility prediction by XGBoost-MLP based on GARCH models

H Dai, G Huang, H Zeng, F Zhou - Journal of cleaner production, 2022 - Elsevier
In recent, air pollution has a sever impact on public health and economy development
throughout the world. Air pollution consists of a variety of harming components, of which fine …

Carbon trading volume and price forecasting in China using multiple machine learning models

H Lu, X Ma, K Huang, M Azimi - Journal of Cleaner Production, 2020 - Elsevier
Motivated by reducing carbon emissions, carbon trading market have been opened to
promote environmental protection. Accurate carbon trading volume and price forecasts have …

A hybrid algorithm for carbon dioxide emissions forecasting based on improved lion swarm optimizer

W Qiao, H Lu, G Zhou, M Azimi, Q Yang… - Journal of Cleaner …, 2020 - Elsevier
Global warming is a hot topic of climate change, and its negative impact on oceans, ecology,
and human health has become an indisputable fact. As a major cause of global warming …

Machine-Learning based methods in short-term load forecasting

W Guo, L Che, M Shahidehpour, X Wan - The Electricity Journal, 2021 - Elsevier
Short-term load forecasting is of great significance to the secure and efficient operation of
power systems. However, loads can be affected by a variety of external impact factors and …

Multivariate empirical mode decomposition based hybrid model for day-ahead peak load forecasting

Y Huang, N Hasan, C Deng, Y Bao - Energy, 2022 - Elsevier
Accurate day-ahead peak load forecasting is crucial not only for power dispatching but also
has a great interest to investors and energy policy maker as well as government. Literature …

Employing categorical boosting (CatBoost) and meta-heuristic algorithms for predicting the urban gas consumption

L Qian, Z Chen, Y Huang, RJ Stanford - Urban Climate, 2023 - Elsevier
This study was conducted on the presentation of a method to improve the forecast of urban
gas consumption based on the weather variables including temperature, pressure, humidity …