Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infra...
This study seeks to improve the accuracy of air conditioning load forecasting to address the challenges of load management in power systems during high-temperature periods in the summer. Given the limitations of traditional forecasting models in capt...
Accurate forecasting of flexible loads can capture the potential of their application and improve the adjustable space of the distribution network. Flexible load data, such as air conditioning (AC) and electric vehicles (EV), are generally included i...
Cooling load prediction is essential for energy-efficient HVAC operation and building energy management. In multifunctional buildings, heterogeneous load characteristics across functional zones increase the complexity of total cooling load prediction...
Household electricity meters equipped with rooftop photovoltaic systems only display net load power data after coupling loads with photovoltaic power, which gives rise to the issue of unknown PV output and load demand. A non-invasive load decompositi...
The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-fre...
Short-term electric load forecasting plays a significant role in the safe and stable operation of the power system and power market transactions. In recent years, with the development of new energy sources, more and more sources have been integrated...
Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in...
User-level short-term load forecasting is crucial for the efficient operation and dispatch of distribution networks. However, accurate forecasting remains challenging because of pronounced load fluctuations and abrupt local variations. To address thi...
Short-term load forecasting is critical to ensuring the safe and stable operation of the power system. To this end, this study proposes a load power prediction model that utilizes outlier correction, decomposition, and ensemble reinforcement learning...
To fully exploit the correlation between the operating states of appliances, an adaptive graph convolutional neural network (AChebNet) for nonintrusive power load decomposition is proposed. An adaptive adjacency matrix is defined to characterize feat...
To address the challenges of identifying electric vehicles (EVs) in user-side scenarios where multi-source load data is coupled with high-penetration distributed photovoltaics (PV), we propose a robust EV identification framework based on net load de...
Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the t...
To enable fine-grained electricity management on the user side under the dual-carbon strategy and address the inherent limitations of traditional non-intrusive load monitoring (NILM) methods in multi-load parallel operation scenarios, this paper prop...
By aiming at the common distributed random dynamic loads in engineering practice, an equivalent identification method that is based on K–L decomposition and sparse representation is proposed. Considering that the establishment of a probability...
In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristical...
The accurate prediction of short-term load is crucial for the grid dispatching department in developing power generation plans, regulating unit output, and minimizing economic losses. However, due to the variability in customers’ electricity co...
With the ongoing reform of electricity markets and the increasing penetration of demand response (DR) resources, load exhibits stronger price-related uncertainty, posing significant challenges to short-term load forecasting. To address this issue, th...
Accurate cooling load prediction is essential for energy-efficient HVAC system operation. However, the stochastic and nonlinear nature of load data challenges conventional neural networks, causing prediction delays and errors. To address this, a nove...
Accurately predicting the cold load of industrial buildings is a crucial step in establishing an energy consumption management system for industrial constructions, which plays a significant role in advancing sustainable development. However, due to d...
Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people’s livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has be...
With the transition of the energy structure and the continuous development of smart grids, short-term user-side load forecasting plays a key role in fine power dispatch and efficient system operation. However, existing parameter optimization methods...
With the increasing complexity of the power system and the increasing load volatility, accurate load forecasting plays a vital role in ensuring the safety of power supply, optimizing scheduling decisions and resource allocation. However, the traditio...
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of suppo...
High energy-consuming industrial parks are characterized by high base-load-to-peak-valley ratios, overlapping production cycles, and megawatt-scale step changes, which significantly complicate short-term load forecasting. To tackle these challenges,...
With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various...
The construction of zero-carbon parks has become an urgent priority. Electric load forecasting plays a decisive role in enabling the efficient operation of industrial parks; however, the complexity of electric load features within the parks has limit...
For operational management of power plants, it is desirable to possess more precise short-term load forecasting results to guarantee the power supply and load dispatch. The empirical mode decomposition (EMD) method and the particle swarm optimization...
Accurate power load forecasting is crucial for ensuring grid stability, optimizing economic dispatch, and facilitating renewable energy integration in modern smart grids. However, real load forecasting is often disturbed by the inherent non-stationar...
Accurate short-term power load forecasting is crucial to maintaining a balance between energy supply and demand, thus minimizing operational costs. However, the intrinsic uncertainty and non-linearity of load data substantially impact the accuracy of...
With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar da...
Against the backdrop of global climate change and energy structure transition, district energy systems have garnered significant attention for their efficiency and sustainability. Accurate load forecasting is crucial for enhancing the operational eff...
In the context of low carbon, achieving accurate forecasting of electrical energy is critical for power management with the continuous development of power systems. For the sake of improving the performance of load forecasting, an improved MNet-Atten...
Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD) are frequently associated with working memory (WM) dysfunction, which is also observed in various neural psychiatric disorders, including depression, schizophrenia, and ADHD. Early de...
In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition us...
To address the challenges in short-term power load forecasting posed by the non-stationary, multi-scale, and nonlinear characteristics of load data, this study proposes a short-term load interval forecasting method based on a Convolutional Neural Net...
Accurate joint forecasting of electricity, cooling, heating, and gas loads is essential to the coordinated operation of integrated energy systems. However, multivariate energy load sequences exhibit strong cross-carrier coupling, non-stationarity, an...
In order to improve the intelligent diagnosis level of an on-load tap-changer’s (OLTC) mechanical condition, a feature extraction method based on variational mode decomposition (VMD) and weight divergence was proposed. The harmony search (HS) algorit...
Short-term load forecasting is an important part of load forecasting, which is of great significance to the optimal power flow and power supply guarantee of the power system. In this paper, we proposed the load series reconstruction method combined i...
Accurate short-term electricity load forecasting is crucial for day-ahead scheduling and secure operation of power systems. However, electricity load series exhibit significant non-stationarity, with complex coupling between low-frequency trends and...
This paper presents an optimized hybrid deep learning model for power load forecasting—QR-FMD-CNN-BiGRU-Attention—that integrates similar day selection, load decomposition, and deep learning to address the nonlinearity and volatility of p...
Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these chang...
Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of p...
Accurate short-term forecasting of power load is essential for the reliable operation of the comprehensive energy systems of ports and for effectively reducing energy consumption. Owing to the complexity of port systems, traditional load forecasting...
As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forec...
A short-term power load forecasting method is proposed based on an improved Sparrow Search Algorithm (ISSA), Variational Mode Decomposition (VMD), and Bidirectional Long Short Term Memory (BiLSTM) neural network. First, the SSA is optimized by combin...
Nonintrusive load monitoring (NILM) analyzes only the main circuit load information with an algorithm to decompose the load, which is an important way to help reduce energy usage. Recent research shows that deep learning has become popular for this p...
Based on the characteristics of offshore pile foundation engineering, this study proposes a novel interpretation method for pile settlement time history signals in Rapid Load Testing (RLT). The approach utilizes Variational Mode Decomposition (VMD) t...
Accurate regional short-term load forecasting is essential to the reliable operation and efficient energy scheduling of smart grids. However, the data needed for collaborative model training are often distributed across regional utilities and grid op...
At present, the non-intrusive load decomposition method for low-frequency sampling data is as yet insufficient within the context of generalization performance, failing to meet the decomposition accuracy requirements when applied to novel scenarios....
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