A systematic review of statistical and machine learning methods for electrical power forecasting with reported mape score

E Vivas, H Allende-Cid, R Salas - Entropy, 2020 - mdpi.com
Electric power forecasting plays a substantial role in the administration and balance of
current power systems. For this reason, accurate predictions of service demands are needed …

[HTML][HTML] Modeling energy demand—a systematic literature review

PA Verwiebe, S Seim, S Burges, L Schulz… - Energies, 2021 - mdpi.com
In this article, a systematic literature review of 419 articles on energy demand modeling,
published between 2015 and 2020, is presented. This provides researchers with an …

Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm

T Gao, D Niu, Z Ji, L Sun - Energy, 2022 - Elsevier
Mid-term electricity demand forecasting plays an important role in ensuring the operational
safety of the power system and the economic efficiency of grid companies. Most studies …

Combination of manifold learning and deep learning algorithms for mid-term electrical load forecasting

J Li, S Wei, W Dai - IEEE Transactions on Neural Networks and …, 2021 - ieeexplore.ieee.org
Mid-term load forecasting (MTLF) is of great significance for power system planning,
operation, and power trading. However, the mid-term electrical load is affected by the …

A review on short‐term load forecasting models for micro‐grid application

VY Kondaiah, B Saravanan… - The Journal of …, 2022 - Wiley Online Library
Load forecasting (LF), particularly short‐term load forecasting (STLF), plays a vital role
throughout the operation of the conventional power system. The precise modelling and …

Predictive analysis of quarterly electricity consumption via a novel seasonal fractional nonhomogeneous discrete grey model: A case of Hubei in China

WZ Wu, H Pang, C Zheng, W **e, C Liu - Energy, 2021 - Elsevier
Accurate electricity consumption forecasting plays a crucial role in electric power systems
and is a challenging task due to its complicated mechanism induced by multiple influential …

NDVI forecasting model based on the combination of time series decomposition and CNN–LSTM

P Gao, W Du, Q Lei, J Li, S Zhang, N Li - Water Resources Management, 2023 - Springer
Normalized difference vegetation index (NDVI) is the most widely used factor in the growth
status of vegetation, and improving the prediction of NDVI is crucial to the advancement of …

Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants

Y Bai, J **e, C Liu, Y Tao, B Zeng, C Li - International Journal of Electrical …, 2021 - Elsevier
Effective electricity consumption forecasting is extremely significant for enterprises' electricity
planning which can provide data support for production decision, thus improving the level of …