[HTML][HTML] Stacking integration algorithm based on CNN-BiLSTM-Attention with XGBoost for short-term electricity load forecasting
Improving the accuracy of electric load forecasting is critical for grid stability, industrial
production, and residents' daily lives. Traditional short-term load forecasting methods often …
production, and residents' daily lives. Traditional short-term load forecasting methods often …
[HTML][HTML] Explainability and interpretability in electric load forecasting using machine learning techniques–A review
Abstract Electric Load Forecasting (ELF) is the central instrument for planning and
controlling demand response programs, electricity trading, and consumption optimization …
controlling demand response programs, electricity trading, and consumption optimization …
Long term load projection in high resolution for all countries globally
Electricity demand modelling is the central and integral issue for the planning and operation
of power systems. Load projection provides important information for electricity network …
of power systems. Load projection provides important information for electricity network …
[HTML][HTML] A hybrid autoformer framework for electricity demand forecasting
Electricity demand forecasting is of great significance to the electricity system and residents'
life, but it is difficult to forecast the electricity demand series because of the influence of …
life, but it is difficult to forecast the electricity demand series because of the influence of …
Self-updating machine learning system for building load forecasting-method, implementation and case-study on COVID-19 impact
Accurate building load forecasting is a challenging task due to the large volume of input
information, their non-linearity and variant nature due to human activities. In this study, we …
information, their non-linearity and variant nature due to human activities. In this study, we …
Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting
Electric load forecasting is crucial in the planning and operating electric power companies. It
has evolved from statistical methods to artificial intelligence-based techniques that use …
has evolved from statistical methods to artificial intelligence-based techniques that use …
Prediction of whole social electricity consumption in Jiangsu province based on metabolic FGM (1, 1) model
S Zhang, L Wu, M Cheng, D Zhang - Mathematics, 2022 - mdpi.com
The achievement of the carbon peaking and carbon neutrality targets requires the
adjustment of the energy structure, in which the dual-carbon progress of the power industry …
adjustment of the energy structure, in which the dual-carbon progress of the power industry …
Short term Markov corrector for building load forecasting system–Concept and case study of day-ahead load forecasting under the impact of the COVID-19 pandemic
In this paper, we present the concept and formulation of a short-term Markov corrector to an
underlying day-ahead building load forecasting model. The models and the correctors are …
underlying day-ahead building load forecasting model. The models and the correctors are …
Cambodia mid-term transmission system load forecasting with the combination of seasonal ARIMA and Gaussian process regression
P Nop, Z Qin - 2021 3rd Asia Energy and Electrical Engineering …, 2021 - ieeexplore.ieee.org
Mid-term load forecasting (MTLF) is crucial for power transmission system planning, safe
operation, and maintenance. It's very significant in a develo** country like Cambodia. In …
operation, and maintenance. It's very significant in a develo** country like Cambodia. In …
Load forecasting for smart grid using non-linear model in Hadoop distributed file system
The conventional electrical grid structure is evolving in recent years, enhancing with new
technology. The new structure of power system 'Smart grid'is trying to get solution for the …
technology. The new structure of power system 'Smart grid'is trying to get solution for the …