Power Load Forecasting Research Articles

Pinball-Huber boosted extreme learning machine regression: a multiobjective approach to accurate power load forecasting

Power load data frequently display outliers and an uneven distribution of noise. To tackle this issue, we present a forecasting model based on an improved extreme learning machine (ELM). Specifically, we introduce the novel Pinball-Huber robust loss function as the objective function in training. The loss function enhances the precision by assigning distinct penalties to errors based on their directions. We employ a genetic algorithm, combined with a swift nondominated sorting technique, for multiobjective optimization in the ELM-Pinball-Huber context. This method simultaneously reduces training errors while streamlining model structure. We practically apply the integrated model to forecast power load data in Taixing City, which is situated in the southern part of Jiangsu Province. The empirical findings confirm the method’s effectiveness.

A Short-Term Power Load Forecasting Approach Based on EMD-SSA-LSTM

Abstract Accurate power load forecasting is the foundation of the power system. However, economic, weather, and geographic factors can affect short-term electric load forecasting and can make it difficult to enhance load forecasting accuracy. To solve the problem, a novel load forecasting method based on empirical mode decomposition (EMD) and long short-term memory (LSTM) neural networks has been proposed. Firstly, the EMD algorithm is utilized to decompose the original signal into elementary components and a series of intrinsic mode functions (IMF). Subsequently, all components are input into the LSTM neural networks for load prediction. Moreover, to further improve the prediction accuracy, the sparrow search algorithm (SSA) is introduced to optimize the parameters of LSTM for the prediction of basic components. Integrating SSA and EMD-LSTM, the EMD-SSA-LSTM model is further proposed for short-term power load forecasting. Finally, the proposed model is validated by several experiments, and the results show that the coefficient of determination (R2) under the proposed model can reach 0.98, the mean absolute error (MAE) is 0.015, and the root mean square error (RMSE) is 0.02. It is verified that the proposed model can improve the accuracy of load forecasting.

A short-term electricity load forecasting method integrating empirical modal decomposition with SAM-LSTM

Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSA- SAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect.

A short-term power load forecasting system based on data decomposition, deep learning and weighted linear error correction with feedback mechanism

Accurate power load forecasting enables Independent System Operators (ISOs) to precisely quantify the demand patterns of users and achieve efficient management of the smart grid. However, with the increasing variety of power consumption patterns, the power load data displays increasingly irregular characteristics, which posing great challenges for accurate load forecasting. In order to solve above problem, a novel power load forecasting system is proposed based on data denoising, customized deep learning and weighted linear error correction. Specifically, we first proposed an improved optimization algorithm IGWO-JAYA which enhanced the Grey Wolf Optimizer (GWO) algorithm by using Halton low-discrepancy sequence and the mechanism of JAYA algorithm. In data denoising, the proposed optimizer was employed to optimize the Variational Mode Decomposition (VMD), enabling data-driven intelligent denoising. The customized deep learning framework contained multi-layer Convolution Neural Network (CNN), Bi-directional Long Short-Term Memory (Bi-LSTM) and Multi-Head Attention mechanism. The effective integration of these layers can significantly improve the capacity for nonlinear fitting of deep learning. In weighted linear error correction, the IGWO-JAYA algorithm was employed to determine the appropriate weight for point forecasting values and residual forecasting values. By weighting them, the forecasting precision has been further enhanced. To verify the forecasting ability of the system, we conducted experiments on power load datasets from four states in Australia and found that it has the best performance compared with all rivals. In the discussion, we demonstrated the convergence efficiency of the IGWO-JAYA algorithm by CEC test function.

Improved dendritic learning: Activation function analysis

This study conducted a thorough evaluation of an improved dendritic learning (DL) framework, focusing specifically on its application in power load forecasting. The objective was to optimise the activation functions within the synapses and somas of DL to enhance their adaptability across diverse real-world scenarios. Through a rigorous analysis involving 25 experiments across five activation functions (sigmoid, hyperbolic tangent (tanh), rectified linear unit (ReLU), leaky ReLU, and exponential linear unit (ELU)), we elucidated their impacts on both regression and classification performance. Notably, the leaky ReLU–tanh combination demonstrated exceptional mean performance and effectiveness across 14 benchmark datasets from the University of California Irvine Machine Learning Repository, surpassing alternative combinations. When applied to power load forecasting, this combination outperformed other models, particularly transformer and LSTM. These findings underscore the significant advantages of the leaky ReLU–tanh-based DL framework in accurately predicting electricity load in smart grids, as evidenced by the lowest mean absolute error (39.27), root mean squared error (29.13), and mean absolute percentage error (2.84).

Deep learning-driven hybrid model for short-term load forecasting and smart grid information management

Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.

Evaluation of electrical load demand forecasting using various machine learning algorithms

The energy sector heavily relies on a diverse array of machine learning algorithms for power load prediction, which plays a pivotal role in shaping policies for power generation and distribution. The precision of power load prediction depends on numerous factors that reflect nonlinear traits within the data. Notably, machine learning algorithms and artificial neural networks have emerged as indispensable components in contemporary power load forecasting. This study focuses specifically on machine learning algorithms, encompassing support vector machines (SVMs), long short-term memory (LSTM), ensemble classifiers, recurrent neural networks, and deep learning methods. The research meticulously examines short-term power load prediction by leveraging Chandigarh UT electricity utility data spanning the last 5 years. The assessment of prediction accuracy utilizes metrics such as normalized mean square error (NMSE), root mean squared error (RMSE), mean absolute error (MAE), and mutual information (MI). The prediction results demonstrate superior performance in LSTM compared to other algorithms, with the prediction error being the lowest in LSTM and 13.51% higher in SVMs. These findings provide valuable insights into the strengths and limitations of different machine learning algorithms. Validation experiments for the proposed method are conducted using MATLAB R2018 software.

Research on medium-term electric power load forecasting based on SSAD-CEEMDAN

To fully enhance the accuracy of electric power load forecasting, a medium-term electric power load forecasting model based on secondary decomposition (predicted model of power load based on secondary decomposition, PMPL-SD) is proposed. In the PMPL-SD algorithm, the original load data are first decomposed and reconstructed using the singular spectrum analysis decomposition method; then, the data after denoising are further decomposed and reconstructed using the noise-assisted complete ensemble empirical mode decomposition to obtain three components: high frequency, medium frequency, and trend. At the same time, a combined network model consisting of convolutional neural networks and bidirectional long short-term memory networks is used to predict the three components separately. The component results are integrated to obtain the final forecasting result. To verify the performance of the PMPL-SD algorithm, three models are selected for comparison. The experimental results show that the proposed algorithm has higher forecasting accuracy in medium-term electric power load forecasting.

Research on Electricity Load Forecasting Based on LSTM Networks and BP Networks

The aim of this study is to compare the performance of Long Short-Term Memory (LSTM) network with Back Propagation Neural Network (BP network) for electricity load forecasting. Two prediction methods are used in the study: the first one uses a sliding window approach to predict the load of the next hour using the electricity load data of the past 24 hours and external factors such as temperature and humidity; the second one uses the data of the same time point of the past 24 days to predict the load of the corresponding time point of the next day. The results show that in the first method, the BP network outperforms the LSTM in prediction; while in the second method, both networks perform poorly, but the LSTM network outperforms the BP network in prediction. This finding provides new insights into power load forecasting, which is important for power system efficiency and reliability improvement.

Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy.

Short-term power load forecasting for integrated energy system based on a residual and attentive LSTM-TCN hybrid network

In the context of Integrated Energy System (IES), accurate short-term power demand forecasting is crucial for ensuring system reliability, optimizing operational efficiency through resource allocation, and supporting effective real-time decision-making in energy management. However, achieving high forecasting accuracy faces significant challenges due to the inherent complexity and stochastic nature of IES’s short-term load profiles, resulting from diverse consumption patterns among end-users and the intricate coupling within the network of interconnected energy sources. To address this issue, a dedicated Short-Term Power Load Forecasting (STPLF) framework for IES is proposed, which relies on a newly developed hybrid deep learning architecture. The framework seamlessly combines Long Short-Term Memory (LSTM) with Temporal Convolutional Network (TCN), enhanced by an attention mechanism module. By merging these methodologies, the network leverages the parallel processing prowess of TCN alongside LSTM’s ability to retain long-range temporal information, thus enabling it to dynamically concentrate on relevant sections of time series data. This synergy leads to improved prediction accuracy and broader applicability. Furthermore, the integration of residual connections within the network structure serves to deepen its learning capabilities and enhance overall performance. Ultimately, results from a real case study of a user-level IES demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed framework on the test set is 2.35%. This error rate is lower than the averages of traditional methods (3.43%) and uncombined single submodules (2.80%).

Technical and economic study of renewable-energy-powered system for a newly constructed city in China

The power and transportation sectors are major carbon emission sources of cities in China, meanwhile, urbanization and industrialization remain the most important impetus for the Chinese economy. Thus, urban-level carbon mitigation is critical for China to realize a low carbon development strategy. This study designs two renewable-energy power-generation dominated power systems for a totally newly built Chinese city, Xiongan, considering emerging technologies, such as vehicle-to-grid as an energy storage facility and geothermal as a local renewable energy resource. The new energy systems are assessed from both economic and carbon emission aspects based on short &long terms power load forecasts. The simulation results shows that the optimal short-term system is a photovoltaic (PV)/geothermal generator/grid purchase/100 Li battery/converter. For the short term, the system's net present cost (NPC) is approximately $8.26B, and the average cost of valuable electrical energy (COE) produced by the system is approximately $0.0942/kWh. The optimal long-term system is a PV/geothermal generator /V2G/ converter, the NPC of the optimal system is approximately $47.7B, and the COE of the system is approximately $0.078/kWh. Energy bill and carbon emissions can both be cut sharply for the city if it adopts these new types of renewable energy dominated systems.

Short-term power load forecasting using SSA-CNN-LSTM method

ABSTRACT The short-term power load forecasting provides an essential foundation for the dispatching management of the power system, which is crucial for enhancing economy and ensuring operational stability. To enhance the precision of the short-term power load forecasting, this paper proposes a hybrid prediction algorithm based on sparrow search algorithm (SSA), convolutional neural network (CNN) and long short-term memory (LSTM). First, feature datasets are constructed based on date information, meteorological data, similar days. The CNN performs effective feature extraction on the data and feeds the results into the LSTM for time series data analysis. Second, eight key parameters are optimized by SSA for improving the prediction precision of the CNN-LSTM prediction model. Simulation results show that the R2 of the proposed model exhibits a substantial enhancement in comparison to other models, reaching 0.9919 and presents a remarkable decrease in MAPE resulting in a value of 1.2%. Furthermore, RMSE and MAE have decreased to 1.17MW and 0.97MW respectively. Therefore, the proposed method has the ability to improve the prediction accuracy, due to the advantages in data mining of CNN, good time series data fitting ability of LSTM, and excellent optimization ability of SSA.

Power Load Forecast Based on CS-LSTM Neural Network

Load forecast is the foundation of power system operation and planning. The forecast results can guide the power system economic dispatch and security analysis. In order to improve the accuracy of load forecast, this paper proposes a forecasting model based on the combination of the cuckoo search (CS) algorithm and the long short-term memory (LSTM) neural network. Load data are specific data with time series characteristics and periodicity, and the LSTM algorithm can control the information added or discarded through the forgetting gate, so as to realize the function of forgetting or memorizing. Therefore, the use of the LSTM algorithm for load forecast is more effective. The CS algorithm can perform global search better and does not easily fall into local optima. The CS-LSTM forecasting model, where CS algorithm is used to optimize the hyper-parameters of the LSTM model, has a better forecasting effect and is more feasible. Simulation results show that the CS-LSTM model has higher forecasting accuracy than the standard LSTM model, the PSO-LSTM model, and the GA-LSTM model.

Research on short-term load forecasting based on clustering and deep learning

In power systems, power load forecasting is essential to ensure the reliability and efficiency of power supply. Since power load is affected by many factors, including weather, seasonality, and social activities, its patterns and changes are complex and diverse, and traditional forecasting methods may make it difficult to meet demand. In this background, this study combines the K-means clustering algorithm and deep learning model. First, through K-means clustering, we grouped historical load data, dates, and temperatures to identify different load patterns. This grouping helps adapt to different load changes, thereby improving the adaptability of the forecast. Then, a deep learning model is applied, combining a convolutional neural network (CNN) and a two-layer bidirectionally gated recurrent unit (BIGRU). These two models are used to deal with spatiotemporal characteristics and sequence dependence in load data respectively. CNN is used to capture the spatial features in the load data, while BIGRU processes the time series information in the data to effectively capture the complex dynamic nature of the load. K-means clustering information is entered as additional data into the CNN and BIGRU models. The experimental results of the study show that by fully considering different load patterns and data characteristics. Reducing load demand and providing more reliable short-term load forecasts improve the efficiency of the power supply, reduce energy waste, and reduce the burden on the power system.

Power short-term load forecasting based on fuzzy C-means clustering and improved locally weighted linear regression

Power load forecasting is an important part of modern smart grid operation management. Accurate forecasting guides the efficient and stable operation of the power system. In this paper, a fuzzy C-means clustering algorithm and an improved locally weighted linear regression model are proposed for short-term power load forecasting. First, the fuzzy C-means clustering algorithm is used to cluster the power load. Make the power consumption behavior of load data of the same category similar and use the power consumption load data of the same category as the training sample. Then, to solve the problem of large calculation and insufficient fitting of the locally weighted linear regression model, the k-nearest neighbor range constraint is introduced into the model for daily load forecasting. Finally, the effectiveness of the method is verified by a simulation example. Experimental results show that this method can effectively improve the accuracy and generalization ability of power load forecasting compared with other methods.

Load forecasting based on dynamic adaptive and adversarial graph convolutional networks

With the widespread integration of renewable energy sources into the power system and the diversification and complexity of the energy consumption patterns, which makes it more difficult to maintain a source-load balance of the power system.In the power systems, the results of the power load forecasting can be used for generator scheduling and load distribution. Therefore, the accurate power load forecasting is critical to operation of the power system. In this paper, a load forecasting model based on a dynamic adaptive and adversarial graph convolutional network (DAAGCN) is proposed, which combines a dynamic adaptive graph generation network (DAGG) and a generative adversarial network (GAN). Firstly, DAGG utilizes integrated time-varying embedding and node embedding to generate dynamic adaptive graphs for inferring spatial–temporal dependencies between different temporal loads. Second, the underlying spatial–temporal prediction model is adversarial trained based on the idea of a zero-sum game. The module parameters are optimized using the L1 loss and the adversarial training loss together as the training objectives of the model. Finally, short-term forecasting of electricity loads was performed on the datasets using DAAGCN, and the results were compared with several other forecasting modules.

Short-term Electricity Load Forecasting Based on Improved Seagull Algorithm Optimized Gated Recurrent Unit Neural Network

INTRODUCTION: The complexity of the power network, changes in weather conditions, diverse geographical locations, and holiday activities comprehensively affect the normal operation of power loads. Power load changes have characteristics such as non stationarity, randomness, seasonality, and high volatility. Therefore, how to construct accurate short-term power load forecasting models has become the key to the normal operation and maintenance of power.OBJECTIVES: Accurate short-term power load forecasting helps to arrange power consumption planning, optimize power usage and largely reduce power system losses and operating costs.METHODS: A hybrid decomposition-optimization-integration load forecasting method is proposed to address the problems of low accuracy of current short-term power load forecasting methods.RESULTS: The original power load time series is decomposed using the complete ensemble empirical modal decomposition method, while the correlation of power load influencing factors is analysed using Pearson correlation coefficients. The seagull optimisation algorithm is overcome to fall into local optimality by using the random adaptive non-linear adjustment strategy of manipulated variables and the differential variational Levy flight strategy, which improves the search efficiency of the algorithm. Then, the The gated cyclic unit hidden layer parameters are optimised by the improved seagull optimisation algorithm to construct a short-term electricity load forecasting model.The effectiveness of the proposed method is verified by simulation experimental analysis. The results show that the proposed method has improved the accuracy of the forecasting model.CONCLUSION: The CEEMD method is used to decompose the original load time series, which improves the accuracy of the measurement model. The GRU prediction model based on improved SOA optimization not only has better prediction accuracy than other prediction models, but also consumes the least amount of time compared to other prediction models.

Refining Short-Term Power Load Forecasting: An Optimized Model with Long Short-Term Memory Network

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.

Editorial for Vol.31, No.3

The September 2023 (Vol. 31, No. 3) issue of CIT. Journal of Computing and Information Technology brings four papers from the areas of telecommunications, power load forecasting, computer vision, and social networks.