Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads
Z Zhang, WC Hong - Knowledge-Based Systems, 2021 - Elsevier
Accurate electric load forecasting is critical in guaranteeing the efficiency of the load
dispatch and supply by a power system, which prevents the wasting of electricity and …
dispatch and supply by a power system, which prevents the wasting of electricity and …
Smart energy management algorithm for load smoothing and peak shaving based on load forecasting of an island's power system
In this study, a novel algorithm for the management of the power flows of an islanded power
system was developed, capable of simultaneously achieving steadier conventional unit …
system was developed, capable of simultaneously achieving steadier conventional unit …
Multi-node load forecasting based on multi-task learning with modal feature extraction
Accurate multi-node load forecasting is the key to the safe, reliable, and economical
operation of the power system. However, the dynamic nature of load and the coupling nature …
operation of the power system. However, the dynamic nature of load and the coupling nature …
Energy disaggregation of appliances consumptions using ham approach
H Liu, Q Zou, Z Zhang - IEEE Access, 2019 - ieeexplore.ieee.org
Non-intrusive load monitoring (NILM) makes it possible for users to track the energy
consumption of a household. In this paper, we present a new hybrid energy disaggregation …
consumption of a household. In this paper, we present a new hybrid energy disaggregation …
Improving the energy efficiency of technological equipment at mining enterprises
R Klyuev, I Bosikov, O Gavrina, M Madaeva… - Energy Management of …, 2019 - Springer
The article presents the results of the economic effect obtained by increasing the productivity
of technological equipment at the mining and processing plant. The purpose of this work is a …
of technological equipment at the mining and processing plant. The purpose of this work is a …
Research on short-term load prediction based on Seq2seq model
G Gong, X An, NK Mahato, S Sun, S Chen, Y Wen - Energies, 2019 - mdpi.com
Electricity load prediction is the primary basis on which power-related departments to make
logical and effective generation plans and scientific scheduling plans for the most effective …
logical and effective generation plans and scientific scheduling plans for the most effective …
Multifactor and multiscale method for power load forecasting
Y Zhang, L Liu, F Yuan, H Zhai, C Song - Knowledge-Based Systems, 2023 - Elsevier
In the era of big data, various factors (particularly meteorological factors) have been
considered in power load prediction, and the result shows a clear discrepancy in timescales …
considered in power load prediction, and the result shows a clear discrepancy in timescales …
A rank analysis and ensemble machine learning model for load forecasting in the nodes of the central Mongolian power system
Forecasting electricity consumption is currently one of the most important scientific and
practical tasks in the field of electric power industry. The early retrieval of data on expected …
practical tasks in the field of electric power industry. The early retrieval of data on expected …