Load Prediction Research Articles

Efficient grid management: smart forecasting of short-term power load using PSO-LSTM

Recent load forecasting techniques combining machine learning models and hyperparameter optimization algorithms have shown success for short-term load forecasting (STLF) task, but they often require complex programming, higher computational costs, and greater parameter tuning. In this paper, we introduce an improved STLF model that combines Long Short-Term Memory (LSTM) neural network with Particle Swarm Optimization (PSO) for enhanced performance. In the proposed approach, the number of hidden neurons in different LSTM layers, learning rate and the number of iterations for training are optimized using the PSO algorithm. To validate the effectiveness of this method, meteorological data and historical load data from a real-world power grid are used as input. The experimental results reveal PSO significantly enhances hyperparameter tuning for LSTM neural networks, leading to improved predictive modelling. The PSO-LSTM model performed better than the LSTM model by more than 20% (in terms of Mean Absolute Error), and showed low sensitivity to hyperparameters. Comparative analysis with alternative approaches from the literature further validates the PSO-LSTM’s effectiveness in STLF. Additionally, the model achieved stable multi-step prediction capabilities, with average errors of 3.6445 for MAE, 4.6509 for RMSE, and 4.6519 for MAPE over a 1–4 day ahead lead times. This study highlights PSO-LSTM’s enhanced robustness and accuracy in power load prediction while addressing hyperparameter tuning challenges through self-optimization.

A Hybrid Model for Short-Term Energy Load Prediction Based on Transfer Learning with LightGBM for Smart Grids in Smart Energy Systems

ABSTRACT Electricity load prediction plays a vital role in energy management. The necessity of combining scattered power generation channels of sustainable energy and other substitute resources into distribution grid networks is increasing because of the 4.1 percent rise in world electricity since 2021. The utilization of probability power generators and energy storage is maximized by short-term load prediction methods, which make it possible to anticipate potential energy usage. To facilitate the growth of energy systems in quite a sustainable way, urban energy planners utilize a range of methods and technological advances, including simulation, probability optimization, comprehensive improvement, and modelling techniques for predicting power requirements. Predicting energy consumption at granular time scales (e.g., hourly or sub-hourly intervals) is common practice for short-term energy load prediction. For similar time-series forecasting challenges, the authors have decided to use transfer learning (TL) with light gradient-boosting machine (LightGBM).1 This model can benefit from models trained on larger data sets through TL, which might boost prediction accuracy. Energy prediction and effective resource utilization largely support the accurate transition of energy systems. In this paper, the authors propose a hybrid model for short-term energy load prediction. Based on TL to optimize LightGBM (OLGBM) for smart grids, The proposed model first applies data pre-processing using an abnormal supplement strategy with an immediate deviation selection technique. This phase eliminates the missing values and extracts the required features. The second phase uses a TL-OLGBM with hyperparameters. The TL method helps to learn the dynamic time scale and complex data patterns, and the hyperparameters are tuned via the Bayesian optimization technique, which addresses the challenge of short-term load forecasting (STLF) and increases forecasting accuracy. Finally, an optimized LightGBM model is applied, combining the time and energy features to predict effective energy forecasting. The proposed model gained a 2.834589 mean absolute percentage error (MAPE) value and a 0.9706495 GINI index after execution on the prescribed data set. We compare the performance of the proposed model with other comparable models, and the simulation outcomes show that the model can generate the best results for MAPE, accuracy, and root-mean-square deviation (RMSE).

Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation

Quantifying Uncertainty with Conformal Prediction for Heating and Cooling Load Forecasting in Building Performance Simulation

Charging station cluster load prediction: Spatiotemporal multi-graph fusion technology

In recent years, single-station charging load prediction technology for electric vehicles has gradually matured, but there are few prediction studies at the charging station cluster level. Therefore, this research propose a load prediction framework for electric vehicle charging station groups based on multi-graph fusion. First, a distance map, a traffic network map, and a traffic density map are established to extract the topological information of the charging station group, and the fusion operation is performed by establishing the relationship between the influencing factors based on the mutual correlation of the influencing factors and the multi-graph attention mechanism; Secondly, a spatiotemporal prediction model was constructed, multi-level feature extraction was performed, and multiple charging stations were predicted at the same time; Finally, taking the cluster load of charging stations in an urban area as an example, a comparative experiment was conducted to compare the model proposed in this study with the mathematical model, the prediction performance of different variants of machine learning models, common deep learning models and the model proposed in this study, and a comparative test with multiple prediction horizons was conducted. The research results show that the model proposed in this work improves the accuracy of multi-station forecasting and provides new ideas for data-driven charging station cluster forecasting research.

Virtual track irregularity establishment for load prediction of heterogeneous bogie frames for virtually coupled trainsets

In this paper, we establish the virtual track irregularity (VTI) considering suspension system dynamics for load prediction of heterogeneous bogie frames for virtual coupled train sets (VCTS). At first, the dynamic characteristics of axlebox spring set and primary suspension vertical damper of A/B-type high-speed trains are analyzed and compared for establishment of frequency-varying parameter simplified vehicle dynamic models (FVP-SVDMs). Then, a frequency-segmented time-domain inversion (FSTIM) of VTI suitable for FVP-SVDMs is conducted, and a quantitative evaluation method for VTI consistency is established. Finally, the consistency of VTIs based on measured loads of A/B-type high-speed train bogie frames is quantitatively assessed, and the agreement between the measured and predicted load spectra based on VTI is evaluated. Based on the FVP-SVDM and the corresponding VTI, the equivalent load amplitude error between the predicted and measured bouncing load is 7%. The VTI technology provides the possibility for VCTS to eliminate the need for conducting the main test before applying the new vehicles on the line. In addition, for the fleet is running on the same track through the VCTS communication, the VTI will provide the theoretical support for implementing active control across the entire fleet.

Optimized load vector regression for load prediction and improvement using trombe walls in household electrical energy consumption

Optimized load vector regression for load prediction and improvement using trombe walls in household electrical energy consumption

An advanced hybrid deep learning model for accurate energy load prediction in smart building

In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph neural network (GNN), Transformer and Fusion Layer architectures for precise energy load forecasting. Better feature extraction results from the Improved-CNN's dilated convolution and residual block accommodation of wide receptive fields reduced the vanishing gradient problem. By capturing temporal links in both directions, Bi-LSTM networks help to better grasp complicated energy use patterns. Graph neural networks improve predictive capacities across linked systems by characterizing the spatial relationships between energy-consuming units in smart cities. Emphasizing critical trends to guarantee reliable forecasts, transformer models use attention methods to manage long-term dependencies in energy consumption data. Combining CNN, Bi-LSTM, Transformer and GNN component predictions in a Fusion Layer synthesizes numerous data representations to increase accuracy. With Root Mean Square Error of 5.7532 Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532 Wh and R2 of 0.9701, the hybrid model fared better than other models on the ‘Electric Power Consumption’ Kaggle dataset. This work develops a realistic model that helps informed decision-making and enhances energy efficiency techniques, promoting energy load forecasting in smart cities.

Short‐term load interval prediction with unilateral adaptive update strategy and simplified biased convex cost function

AbstractThis article proposes a unilateral Adaptive update strategy based Interval Prediction (AIP) model for short‐term load prediction, which is developed based on lower and upper bound estimation (LUBE) architecture. In traditional LUBE interval prediction model, the model training is usually trained by heuristic algorithms. In this article, the model training is formulated as a bi‐level optimization problem with the help of proposed unilateral adaptive update strategy and cost function. In lower‐level problem, a simplified biased convex cost function is developed to supervise the learning direction of basic prediction engines. The basic prediction engine utilizes Gated Recurrent Unit (GRU) to extract features and Full connected Neural Network (FNN) to generate interval boundary. In upper‐level problem, a unilateral adaptive update strategy with unilateral coverage rate is put forward. It iteratively tunes hyper‐parameters of cost function during training process. Comprehensive experiments based on residential load data are implemented and the proposed interval prediction model outperforms the tested state‐of‐the‐art algorithms, achieving a 15% reduction in prediction error and a 20% decrease in computational time.

Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load

To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R2 coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R2 coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance.

Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network

Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network

Phase-field approach for fracture prediction of brittle cracked components

The versatility of the phase-field method (PFM) in predicting elusive fracture trajectories has attracted researchers in recent years; however, limited focus has been directed toward fracture load prediction for brittle materials. This study presents a computationally low-cost and straightforward framework based on phase-field modeling to predict the fracture loads and fracture initiantion angles of brittle cracked components under in-plane loading conditions. The framework was assessed through a comprehensive comparison between numerical and experimental data for various specimens and test configurations. Despite using 1D analytical formulations for calculating phase-field parameters, great accordance between empirical data and simulation results demonstrated the effectiveness of the proposed model. A technique that restricts the computation of the phase-field variable merely around the crack tip was applied to significantly reduce the computational costs of PFM simulations. Furthermore, we examined different numerical aspects and confirmed the robustness of the proposed procedure.

When wavelet decomposition meets external attention: a lightweight cloud server load prediction model

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.

Capacity reinstatement of reinforced concrete one-way ribbed slabs with rib-cutting shear zone openings: Hybrid fiber reinforced polymer/steel technique

This study examined the use of two configurations for capacity restoration of reinforced concrete (RC) one-way ribbed slabs containing openings in shear zones. Four specimens of half-scale comprised three ribs in addition to a top RC slab. The test plan included a control specimen without openings, one with two rib-cutting shear openings, one strengthened using a blend of carbon FRP (CFRP) composites and steel plates, and another retrofitted with a combination of glass FRP (GFRP) composites and steel plates. The two strengthening schemes were found successful at fully restoring the ultimate load of the specimens. The ultimate load of specimen strengthened using the hybrid CFRP/steel system exceeded the control slab without openings by 52%. However, in the other specimen where a mix of steel plates and GFRP sheets was used, the load capacity was only 5% less than the control specimen without openings. While the dissipated energy and stiffness were reinstated and improved for the hybrid CFRP/steel system, they were partially restored for the GFRP/steel system. Additionally, a prediction approach was developed to estimate the maximum load of the slabs. The developed approach considered potential shear and flexural modes of failure, providing close predictions of the ultimate load.

Summary of Liquid Loading Law and Liquid Loading Prediction Model in Gas Well Wellbore

Summary of Liquid Loading Law and Liquid Loading Prediction Model in Gas Well Wellbore

Short-Term Power Load Forecasting Based on Secondary Cleaning and CNN-BILSTM-Attention

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods.

Smart grid stability prediction model using two-way attention based hybrid deep learning and MPSO

In smart grid management, precise stability prediction is a complicated task that adds to the effective allocation of resources with grid stability. Specifically, demand-side management is considered an essential element of the overall Smart Grids system. Hence, predicting future energy demands is crucial to regulating consumption by aligning utility offerings with consumer demand. This research presents a hybrid deep learning model (Convolutional Neural Network [CNN] with Bi-LSTM) with a two-way attention method and a multi-objective particle swarm optimization method (MPSO) for short-term load prediction from a smart grid. The proposed hybrid model utilizes a two-way attention method at its encoding and decoding stages, in which an encoding attention layer helps to recognize all the essential features from an input vector, and a decoding attention layer helps to resolve the fixed context vector problem by offering better memory capacity. A CNN and Bi-LSTM are used to capture the essential features from the dataset. We also utilize a t-Nearest Neighbours algorithm to pre-process the initial dataset. An MPSO method combines the features of CNN and Bi-LSTM methods, resulting in better prediction accuracy. As far as we know, it is the first work to suggest a dynamic short-term load prediction model that considers different significant features and enables precise predicting outcomes. The performance of the proposed model and existing well-known deep learning models such as Recurrent Neural Network, Gated Recurrent Unit, Long Short-Term Memory (LSTM), Time Series Transformer, CNN-LSTM and various performance measuring parameters MAE, MSE, MAPE and RMSE are calculated on online UCI dataset (Electrical Grid Stability Simulated Dataset). The proposed hybrid model achieved a better prediction result, which proves the efficiency of the proposed model.

MGMI: A novel deep learning model based on short-term thermal load prediction

Accurate heat load prediction is the key to stable operation and control of district heating system. However, current heat load forecasting methods tend to treat individual buildings as isolated entities, ignoring the temporal and spatial correlation between buildings. In this paper, a heat load forecasting model is proposed which fully considers spatiotemporal information. Firstly, the spatial and temporal relationship between buildings is analyzed by statistical method. Secondly, synchronous wavelet transform (SWT) is used to denoise the heat load data to reduce the difficulty of prediction. Then, a hybrid model of multi-modal graph attention network (MG) and multi-scale Informer (MI) is constructed to capture spatiotemporal information in the data. Finally, an example of DHS in a campus in Liaoyang is analyzed. The results showed that compared with LSTM, CNN-Informer, GCN-LSTM and GAT-Informer, the MAE of MGMI on 168 h test set was reduced by 71.4%, 61.5%, 58% and 41.6%, respectively. And has the highest computational efficiency. The validity of the model is verified, which provides an important basis for the operation control of the thermal system.

A simple load model based on hybrid mechanism and data-driven approach for district heating in building complex

Accurate prediction of heating loads in district heating systems is essential for the implementation of demand-driven heating. This work presents a novel heating load prediction model that is particularly suitable for complex multi-user buildings. The input characteristics of the model are established through the heat transfer mechanism, considering factors such as the cumulative impact of outdoor temperature and user demand (indoor temperature). The specific form of the heating load function is determined using the MLR-PSO (Multiple Linear Regression-Particle Swarm Optimization) method. Only the indoor and outdoor temperatures need to be provided for the model to calculate future heating loads. Practical engineering tests demonstrated that the model achieved normalized mean bias errors of daily loads between 4.98 % and 5.54 % across different heating seasons, with a minimum annual relative deviation of 0.75 % for annual loads. Additionally, the model helps guide the operation of heating systems. For example, during the 2021–2022 heating season, setting the target indoor temperature at 18 °C reduced weekly energy consumption by 15.3 % compared to the previous season. This approach may be employed to construct a simple load model for existing heating systems to accurately predict both short-term and long-term loads, providing valuable insights into the management and control of heating systems.

Scale-specific controls of monthly suspended sediment load in a typical inland river

Sediment transport is a multi-scale and non-linear process controlled by multiple variables. Given the intricate nature of sediment transport mechanisms and the overlap of different factors’ effects on it at various time scales, unraveling the relationship between sediment load and its influencing factors is challenging. This study aimed to elucidate the multi-timescale effects of factors on monthly suspended sediment load in a seasonal inland river. In this study, monthly sediment loads measured by the depth-integrating method were obtained from the Tabu River Basin in northern China from 2007 to 2021, alongside data on four controlling factors: runoff, precipitation, water surface evaporation, and the normalized difference vegetation index (NDVI). The results showed that sediment load, runoff, and precipitation showed strong variability during 2007–2021, while water surface evaporation and NDVI exhibited moderate variability. Multivariate empirical mode decomposition (MEMD) decomposed the temporal patterns of sediment load and associated variables into five intrinsic mode functions (IMF) and a residue. IMF1 (4.7 months), IMF2 (9.0 months) and IMF3 (14.2 months) collectively explained 115.88 %, 91.73 %, 91.28 %, 107.69 %, and 106.09 % of the total variability in sediment load, runoff, precipitation, water surface evaporation, and NDVI, respectively. Subsequently, the time-dependent intrinsic correlation (TDIC) was utilized to illustrate the inherent relationships between sediment load and its controlling factors across various time scales. The associations between sediment load and the controlling factors changed significantly with the scale. Runoff was the dominant factor controlling sediment load at IMF1 (4.7 months), IMF2 (9.0 months), IMF3 (14.2 months), and IMF4 (25.1 months). Water surface evaporation controlled the sediment transport process at IMF5 (63.6 months). The prediction of sediment load on the measurement scale, using scale-specific controls analyzed with MEMD (R2 = 0.993), demonstrated superior performance compared to traditional multiple linear regression prediction (R2 = 0.748). This study will provide a theoretical basis for effective prevention and control of soil erosion and watershed management in inland river basins.

Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine.

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.