Short-term Load Forecasting Research Articles

Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.

A novel approach to predict buildings load based on deep learning and non-intrusive load monitoring technique, toward smart building

Short-term load forecasting is critical in managing energy production, domestic consumption, and grid integration. Short-term load forecasting is an essential tool for managing and controlling the processes required to make buildings smarter. Short-term load forecasting at this level of the network is extremely difficult due to the high uncertainty in load demand. This study presents a novel approach for predicting building loads using deep learning and non-intrusive load monitoring (NILM) techniques, aimed at enhancing smart building energy management. The methodology involves the integration of Recurrent Neural Networks (RNNs) to effectively capture temporal patterns in power consumption data, which are critical for accurate load forecasting. The model uses a feature matrix created from power signals to improve its forecast accuracy. The results show that the suggested model greatly outperforms standard models, with an overall accuracy of 97.2 %. The model's robustness is proven through comprehensive experiments, which demonstrate higher performance in short-term load forecasts. Furthermore, a comparison with existing models demonstrates the proposed approach's efficacy in recognizing and predicting load fluctuations, making it a useful tool for smart grid applications.

A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems

Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation.

Centralised vs. decentralised federated load forecasting in smart buildings: Who holds the key to adversarial attack robustness?

The integration of AI and ML into energy forecasting is crucial for modern energy management. Federated Learning (FL) is particularly noteworthy because it enhances data privacy and facilitates collaboration among distributed energy resources. It enables model training across multiple locations while minimizing reliance on centralized servers and data transfers. However, FL faces significant security challenges, particularly from adversarial attacks that can undermine the models' integrity and reliability. This paper addresses these security concerns by evaluating the effectiveness of Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL) in distributed load forecasting. We conducted a comparative analysis using two publicly available datasets, household data and smart grid data, for short-term load forecasting. Our study finds that DFL is more robust against adversarial attacks and artificial noise compared to CFL. Specifically, adversarial model poisoning attacks impact only the targeted client in DFL, while CFL is more broadly affected. For instance, in the presence of attacks, CFL's average client Mean Absolute Error (MAE) increased from 0.084 to 0.78 kWh for Dataset 1 and from 0.475 to 11.159 MW for Dataset 2, whereas DFL maintained relatively low error rates. Conversely, artificial noise had a less pronounced effect on CFL compared to DFL. Additionally, we introduce Decentralized Random Layer Aggregation (DRLA) to enhance DFL's robustness, offering further insights into improving FL methodologies in the energy sector. Our findings also indicate that only a single layer of the neural network is required for local updates during the aggregation process in DFL.

VMD-BiGRU for Short-term Power Load Forecasting with Energy Valley Optimizer Enhancement

Abstract Electrical load, given context of this modern era, is recognized to have distinct characteristics: diversity, flexibility, and inherently, non-linearity and temporality. Aiming to leverage these characteristics and counteract the challenges within, we propose an electrical load forecasting method, namely EVO-VMD-BiGRU. The proposed technique initiates by utilizing Variational Mode Decomposition (VMD) for the breakdown of the load data into constituent modes, extract the principal modes, and merge them with the features of air temperature, wind speed, precipitation, electricity price and holidays. The modes are then fed to the BiGRU model for training to forecast. The Energy Valley Optimizer (EVO) is applied to search for optimal hyperparameters of the BiGRU model, with the objective of minimizing the RMSE, in order to obtain models for each mode. Finally, the method utilizes these models to forecast the values of each mode, summing them to generate the final predictions. T The efficacy of this approach was confirmed through testing on load datasets sourced from Singapore and Australia. The proposed method achieved an RMSE of 81.20 MW and 30.15 MW on the respective datasets, the MAPE of 0.79% and 0.47%, and the R-squared (R2) of 0.99 and 0.99, which are superior to the alternative methods in the experiments. Results show that the proposed method achieves superior outcomes in short-term electricity load forecasting and exhibits promising potential for practical use.

Short-term load forecasting by GRU neural network and DDPG algorithm for adaptive optimization of hyperparameters

Short-term load forecasting (STLF) is critical to optimizing power system operation. Deep learning (DL) methods can provide extremely high accuracy for STLF. However, most models in existing research lack adaptive optimization capabilities in the prediction process and suffer from performance degradation. To resolve the above difficulties, we propose a hybrid model (DDPG-GRU) based on gated recurrent units and deep deterministic policy gradients for STLF. First, the GRU network has the advantage of processing multiple time series inputs and can simultaneously consider multi-dimensional load characteristics, thereby making the model more efficient. Since the GRU model structure is relatively complex, choosing a good set of hyperparameters is very difficult. Therefore, the purpose of using DDPG is to optimize the hyperparameters of the GRU model adaptively. The proposed model is a combination of DL methods and reinforcement learning. In order to prove the superiority of the proposed model, it is applied to the load data of Area 1 in China to perform single-step and multi-step load forecasting, respectively. The results show that DDPG-GRU has a better fitting effect than the baseline method. Taking the multi-step prediction results as an example, compared with the classic GRU network, the MAPE, MAE, and RMSE of the proposed model are reduced by 22.75 %, 14.44 %, and 14.02 %, respectively, while the R2 coefficient is increased by 13.23 %. At the same time, we use the China Area 2 data set to verify the universality of the proposed model. Furthermore, we compared the proposed method with state-of-the-art methods and achieved better accuracy.

Short term load forecasting analysis using machine learning method: An SVM based study

Short-Term Load Forecasting (STLF) is crucial for effective energy management, enabling utilities to optimize electricity generation, distribution, and pricing strategies. This study explores the application of three machine learning models—Linear Regression (LR), Artificial Neural Networks (ANN), and Support Vector Machines (SVM)—to predict short-term electrical load demand. Each model was trained using historical load data enriched with temporal features to capture daily, seasonal, and other variations in electricity consumption. The SVM model demonstrated strong predictive capability, achieving a Mean Absolute Error (MAE) of 1887.41 MW, Mean Squared Error (MSE) of 6942.36 GW, Root Mean Squared Error (RMSE) of 2634.83 MW, and an R2 score of 91.95%. In comparison, the ANN model showed slightly higher errors, while the LR model had the highest error rates, indicating its limitations in capturing non-linear relationships. The results suggest that SVM and ANN models are more effective than LR for STLF due to their ability to handle non-linear dependencies and high-dimensional data. This study highlights the potential of machine learning techniques in enhancing the accuracy and reliability of load forecasting, ultimately supporting better decision-making in energy management.

Short-term power load forecasting in China: A Bi-SATCN neural network model based on VMD-SE.

This study focuses on improving short-term power load forecasting, a critical aspect of power system planning, control, and operation, especially within the context of China's "dual-carbon" policy. The integration of renewable energy under this policy has introduced complexities such as nonlinearity and instability. To enhance forecasting accuracy, the VMD-SE-BiSATCN prediction model is proposed. This model improves computational efficiency and reduces prediction errors by analyzing and reconstructing sequence component complexity using sample entropy (SE) following variational mode decomposition (VMD). Additionally, a self-attention mechanism is integrated into the temporal convolutional network (TCN) to overcome the traditional TCN's limitations in capturing long-term dependencies. The model was evaluated using data from the China Ninth Electrical Attribute Modeling Competition and validated with real-world data from a specific county in Shijiazhuang City, Hebei Province, China. Results indicate that the VMD-SE-BiSATCN model outperforms other models, achieving a mean absolute error (MAE) of 92.87, a root mean square error (RMSE) of 126.906, and a mean absolute percentage error (MAPE) of 0.81%.

MLFGCN: short-term residential load forecasting via graph attention temporal convolution network.

Residential load forecasting is a challenging task due to the random fluctuations caused by complex correlations and individual differences. The existing short-term load forecasting models usually introduce external influencing factors such as climate and date. However, these additional information not only bring computational burden to the model, but also have uncertainty. To address these issues, we propose a novel multi-level feature fusion model based on graph attention temporal convolutional network (MLFGCN) for short-term residential load forecasting. The proposed MLFGCN model fully considers the potential long-term dependencies in a single load series and the correlations between multiple load series, and does not require any additional information to be added. Temporal convolutional network (TCN) with gating mechanism is introduced to learn potential long-term dependencies in the original load series. In addition, we design two graph attentive convolutional modules to capture potential multi-level dependencies in load data. Finally, the outputs of each module are fused through an information fusion layer to obtain the highly accurate forecasting results. We conduct validation experiments on two real-world datasets. The results show that the proposed MLFGCN model achieves 0.25, 7.58% and 0.50 for MAE, MAPE and RMSE, respectively. These values are significantly better than those of baseline models. The MLFGCN algorithm proposed in this paper can significantly improve the accuracy of short-term residential load forecasting. This is achieved through high-quality feature reconstruction, comprehensive information graph construction and spatiotemporal features capture.

Stochastic configuration networks for short-term power load forecasting

The accuracy of power load forecasting can enhance the operational efficacy and economic efficiency of the power system. This paper utilizes the stochastic configuration network (SCN) and improved marine predator algorithm (IMPA) for power systems short-term load forecasting. First, the daily load data is clustered using the kernel fuzzy C-means (KFCM), and the clustering results are utilized as training input data for the load forecasting model. Then, load forecasting is performed using SCN. Next, IMPA is developed by integrating the opposite-based differential evolution (ODE) and normal mutation perturbation strategy for the purpose of preventing the algorithm from converging to local minima. Finally, IMPA is employed to optimize the critical parameters of the load forecasting model. The experimental results clearly demonstrate that the proposed algorithm performs favorably in terms of the prediction accuracy compared to other modeling algorithms.

Short-Term Load Forecasting for Regional Smart Energy Systems Based on Two-Stage Feature Extraction and Hybrid Inverted Transformer

Accurate short-term load forecasting is critical for enhancing the reliability and stability of regional smart energy systems. However, the inherent challenges posed by the substantial fluctuations and volatility in electricity load patterns necessitate the development of advanced forecasting techniques. In this study, a novel short-term load forecasting approach based on a two-stage feature extraction process and a hybrid inverted Transformer model is proposed. Initially, the Prophet method is employed to extract essential features such as trends, seasonality and holiday patterns from the original load dataset. Subsequently, variational mode decomposition (VMD) optimized by the IVY algorithm is utilized to extract significant periodic features from the residual component obtained by Prophet. The extracted features from both stages are then integrated to construct a comprehensive data matrix. This matrix is then inputted into a hybrid deep learning model that combines an inverted Transformer (iTransformer), temporal convolutional networks (TCNs) and a multilayer perceptron (MLP) for accurate short-term load forecasting. A thorough evaluation of the proposed method is conducted through four sets of comparative experiments using data collected from the Elia grid in Belgium. Experimental results illustrate the superior performance of the proposed approach, demonstrating high forecasting accuracy and robustness, highlighting its potential in ensuring the stable operation of regional smart energy systems.

Economic Load Forecasting based on IPSO-VMD-DBiLSTM Network

Accurate short-term load forecasting is vital for the stable operation of power systems. Given the highly volatile and nonlinear nature of load sequences, we propose a novel Deep Bidirectional Long Short-Term Memory (DBiLSTM) network incorporating Variational Mode Decomposition (VMD) and an attention mechanism to address the aforementioned challenges. In this study, the model's hyperparameters are optimized using an Improved Particle Swarm Optimization (IPSO) technique. Notably, IPSO employs a nonlinearly decreasing inertia weight to overcome the drawbacks of premature convergence and local optima.

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).

Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

Fusion of Hierarchical Optimization Models for Accurate Power Load Prediction

Accurate power load forecasting is critical to achieving the sustainability of energy management systems. However, conventional prediction methods suffer from low precision and stability because of crude modules for predicting short-term and medium-term loads. To solve such a problem, a Combined Modeling Power Load-Forecasting (CMPLF) method is proposed in this work. The CMPLF comprises two modules to deal with short-term and medium-term load forecasting, respectively. Each module consists of four essential parts including initial forecasting, decomposition and denoising, nonlinear optimization, and evaluation. Especially, to break through bottlenecks in hierarchical model optimization, we effectively fuse the Nonlinear Autoregressive model with Exogenous Inputs (NARX) and Long-Short Term Memory (LSTM) networks into the Autoregressive Integrated Moving Average (ARIMA) model. The experiment results based on real-world datasets from Queensland and China mainland show that our CMPLF has significant performance superiority compared with the state-of-the-art (SOTA) methods. CMPLF achieves a goodness-of-fit value of 97.174% in short-term load prediction and 97.162% in medium-term prediction. Our approach will be of great significance in promoting the sustainable development of smart cities.

Short-term load forecasting: cascade intuitionistic fuzzy time series—univariate and bivariate models

AbstractShort-term load forecasting (STLF) is essential for developing reliable and sustainable economic and operational strategies for power systems. This study presents a forecasting model combining cascade forward neural network (CFNN) and intuitionistic fuzzy time series (IFTS) models for STLF. The proposed cascading intuitionistic fuzzy time series forecasting model (C-IFTS-FM) offers the advantage of CFNN using the links of both linear and nonlinear to model fuzzy relations between inputs and outputs. Moreover, it offers a more reliable and realistic approach to uncertainty, taking notice of also the degree of hesitation. C-IFTS-FM works in univariate structure when it uses only hourly load data, and in bivariate structure when it uses hourly load data and hourly temperature time series together. The conversion of time series into IFTS is realized with intuitionistic fuzzy c-means (IFCM). Thus, the membership and non-membership values for each data point are produced. In modelling process, membership and non-membership values, in addition to actual lagged observations, are used as input of the CFNNs. The effectiveness of C-IFTS-FM on test sets for both structures was discussed comparatively via different error criteria, in addition, the convergence time was examined, and also the fit of forecasts and observations was presented with different illustrations. Among different combinations of hyperparameters, in the best case, approximately 86% better accuracy is achieved than the best of the others, while even in the case of the worst of hyperparameters combination, the accuracy was improved by over 20% for the PSJM data sets. For HEXING, CHENGNAN, and EUNITE data sets, these progress rates reached approximately 90% in the best case.

Multiscale-integrated deep learning approaches for short-term load forecasting

Accurate short-term load forecasting (STLF) is crucial for the power system. Traditional methods generally used signal decomposition techniques for feature extraction. However, these methods are limited in extrapolation performance, and the parameter of decomposition modes needs to be preset. To end this, this paper develops a novel STLF algorithm based on multi-scale perspective decomposition. The proposed algorithm adopts the multi-scale deep neural network (MscaleDNN) to decompose load series into low- and high-frequency components. Considering outliers of load series, this paper introduces the adaptive rescaled lncosh (ARlncosh) loss to fit the distribution of load data and improve the robustness. Furthermore, the attention mechanism (ATTN) extracts the correlations between different moments. In two power load data sets from Portugal and Australia, the proposed model generates competitive forecasting results.

WITHDRAWN: Short-term Load Forecasting Method Based on LSTM Optimized by the Hybrid Dung Bettle Optimization Algorithm

Forecasting electricity load is a crucial responsibility for the power system. Precise short-term power load forecasting (STLF) is a valuable tool for regional production planning, energy conservation, emission reduction, and grid coordination and dispatch. This study suggests a model for STLF that makes use of an optimized Long and Short-Term Memory (LSTM) neural network through the Hybrid Dung Beetle Optimization algorithm (HDBO-LSTM). Firstly, the Hybrid Dung Beetle Optimization (HDBO) algorithm is used to optimize the LSTM network's hyperparameters to address the problem that the LSTM network's random hyperparameter selection will materially influence the accuracy of load forecasting. Secondly, to address the problems that the original Dung Beetle Optimization (DBO) algorithm has poor global exploration ability and is easy to falls into local optimization, multiple improvement strategies are proposed to enhance the DBO algorithm, and the HDBO algorithm, which has a stronger searching ability and faster convergence speed, is proposed. By comparing with several algorithms on 10 benchmark functions, HDBO shows better search performance. Finally, the HDBO-LSTM load forecasting model is developed and evaluated using the Electrotechnical Mathematical Modeling Competition's power load dataset. The outcomes show that HDBO-LSTM outperforms the other comparison models in terms of accuracy and predicting efficacy.

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting.

Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This article proposes a novel hybrid hierarchical deep-learning (DL) model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small dataset. A multilayer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model's performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.