Accurate Load Forecasting Research Articles

Short-Term Electrical Load Forecasting Based on Fuzzy Rough Set Feature Selection and Multi-kernel Extreme Learning Machine

As the complexity of power systems increases, accurate load forecasting becomes crucial. This paper proposes a method for short-term electrical load forecasting that integrates fuzzy rough set (FRS) theory and multi-kernel extreme learning machine (MKELM) to improve both the accuracy and reliability of load predictions. First, we introduce the FRS theory for pre-selecting features. Next, we use correlation analysis (CA) to get rid of redundant features and choose the most important ones as prediction targets. Second, we introduce a novel prediction model based on the multi-kernel extreme learning machine (MKELM), utilizing an enhanced differential evolution algorithm (DEA) to optimize the kernel function’s parameters and the model’s weights. This approach allows for effective adaptation to various feature subsets. Experimental results on actual power load data demonstrate that our approach achieves high accuracy and reliability in short-term load forecasting. Moreover, comparative evaluations reveal that the proposed method outperforms alternative prediction models on key metrics. ANOVA and multiple comparisons further validate the statistical significance and superiority of the proposed method.

Sparse dynamic graph learning for district heat load forecasting

Accurate heat load forecasting is crucial for the efficient operation and management of district heating systems. This study introduces a novel Sparse Dynamic Graph Neural Network (SDGNN) framework designed to address the complexities of forecasting heat load in district heating networks. The proposed model represents the district heating network as a dynamic graph, with nodes corresponding to consumers or heat sources and edges denoting temporal dependencies. The SDGNN framework comprises three key components: (1) a sparse graph learning module that identifies the most relevant nodes and edges, (2) a spatio-temporal memory enhancement module that captures both short-term and long-term dependencies, and (3) a temporal fusion module that integrates node representations into a comprehensive global forecast. Evaluated on a real-world district heating dataset from Denmark, the SDGNN model demonstrates superior accuracy and efficiency compared to existing methods. The results indicate that the SDGNN framework effectively captures intricate spatio-temporal patterns in historical heat load data, achieving up to 5.7% improvement in RMSE, 7.4% in MAE, and 5.7% in CVRMSE over baseline models. Additionally, incorporating meteorological factors into the model further enhances its predictive performance. These findings suggest that the SDGNN framework is a robust and scalable solution for district heat load forecasting, with potential applications in other domains involving spatio-temporal graph data.

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.

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.

Optimization of Double Exponential Smoothing Using Particle Swarm Optimization Algorithm in Electricity Load

Electricity load forecasting plays a critical role in ensuring the efficient allocation of resources, maintenance optimization, and uninterrupted power supply. The double exponential smoothing (DES) method is widely used in forecasting time series data due to its adaptability and robustness, particularly in handling linear trends without seasonal patterns. However, determining the optimal value of the alpha parameter in DES is crucial for accurate forecasting results. This study proposes the use of the Particle Swarm Optimization (PSO) algorithm to optimize the alpha parameter in DES for electricity load forecasting. PSO is a computational method that iteratively improves candidate solutions by moving particles in the search space based on simple mathematical formulas. By optimizing the alpha parameter using PSO, we aim to enhance the accuracy of short-term electricity load forecasts. Our results demonstrate that the PSO-optimized DES approach achieves a Mean Absolute Percentage Error (MAPE) of 2.89% and an accuracy of 97.11%, indicating significant improvements in forecasting performance. While the PSO algorithm provides promising results, future research may explore the application of other metaheuristic algorithms, such as the whale or orca algorithms, to further enhance the optimization of DES parameters for electricity load forecasting. This study contributes to the advancement of forecasting techniques in the power industry, facilitating more efficient power generation and distribution planning.

Deepening Intelligent Microgrid Management: A Study on Improving Load Forecasting Accuracy Based on Informer Models

Deepening Intelligent Microgrid Management: A Study on Improving Load Forecasting Accuracy Based on Informer Models

LES-based validation of a dynamic wind farm flow model under unsteady inflow and yaw misalignment

This work presents the validation of an extended version of the control-oriented, dynamic wind farm flow solver SPLINTER. The two-dimensional model is applied to use cases of wake steering by yaw misalignment and inflow wind direction variations and the results are compared to large-eddy simulations (LES). While SPLINTER is able to reproduce the antagonal behaviour of decreasing upstream and increasing downstream turbine power under wake deflection, a systematic deviation of the downstream power is detected and quantified, which is connected to underrepresented three-dimensional wake effects. In case of changing inflow wind direction, SPLINTER is capable of computing movement and shape of the bending wakes. The model smooths small-scale turbulent structures and disturbances and does not reproduce wake meandering, but manages to describe the evolution of the mean flow, which is tested by averaging over an ensemble of LES and comparing the resulting flow fields and turbine power time series. Under dynamic inflow conditions, SPLINTER is able to predict at which time intervals and at which rates downstream turbines will be influenced by wakes, which can improve the accuracy of short-term power and load forecasting and enables its application to online model predictive wind farm control.

Residential electricity prediction based on GA-LSTM modeling

With residential electricity demand increasing year after year due to population growth and infrastructure expansion, accurate electricity load forecasting is critical to the operation of power systems. Although certain traditional machine learning models have achieved better results in several fields, their prediction accuracy for energy sources such as electricity is still unsatisfactory. The nonlinear and non-smooth consumption patterns of residential electricity make accurate prediction of electricity loads even more challenging. In this paper, we propose an attention-based competitive stochastic search method for training Long Short-Term Memory (LSTM) networks to predict electricity energy consumption. On the one hand, an attention layer is added to the traditional LSTM to assign feature weights; on the other hand, a genetic algorithm is introduced for stochastic search to prevent convergence to a local optimum during the training process. The experimental results show that the proposed model combines genetic algorithm and LSTM, and the prediction accuracy of the proposed model is higher than other existing prediction models in terms of root mean square error (RMSE), mean absolute error (MAE), r-square(R2), and mean absolute percentage error (MAPE), which are 0.6056, 0.3726, 0.8517, and 0.1856, respectively. In addition, the model also achieves excellent results in short-term prediction, which is better than the traditional LSTM, and the accuracy is significantly improved. It is proved that this method is effective and feasible in the prediction of power energy consumption.

A VMD-deep learning approach for individual load monitoring and forecasting for residential buildings energy management

This paper presents a comprehensive solution for Multi-Individual load disaggregation and Multi-individual load Forecasting in residential energy systems. The approach combines Variable Mode Decomposition (VMD) and deep learning algorithms, incorporating feature selection using Random Forest Regressor and parameter optimization through the Salp Swarm Algorithm (SSA). Correlation analysis is used to identify the most relevant Intrinsic Mode Functions (IMFs) for each appliance, while self-correlation is used to determine the past window for forecasting appliances’ usage a day ahead. The robustness of VMD against noise contributes to the improved accuracy of appliance designation. The load disaggregation strategy yields significant results, indicating the high performance of the disaggregation tasks of these models. Specifically, the proposed disaggregation models exhibit an average RMSE and MAPE of 4.10 W and 4.14 %, respectively. Furthermore, we conduct a comparison among three deep learning architectures (GRU, LSTM, 1DCNN) based Multi-Individual load forecasting models, revealing that the GRU-based MILF model outperforms the other two models, it shows an average performance of RMSE and MAPE of 9.54 W and 8.10 %. The research provides valuable insights and a practical solution for accurate load disaggregation and forecasting, contributing to enhanced energy efficiency, cost savings, and sustainable power consumption in residential settings, and give detailed energetic bill with the aim to analyze the occupant consumption behavior, and gives depth insights about power consumption and cost of each appliance and their operation time.

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.

Day-ahead load forecast based on Conv2D-GRU_SC aimed to adapt to steep changes in load

With the significant increase in the proportion of volatile new energy in the power system in recent years, the difficulty of system scheduling has increased. Accurate load forecasting is an important prerequisite for flexible scheduling. The load itself is a highly regular object that is relatively easy to predict. However, steep changes in load can cause significant deviations in load forecasting. In response to this issue, this article first selects input variables that can help the model identify steep changes in load based on Pearson correlation coefficient and the proposed “Steep change impact rate”. Then, Conv2D-Gate Recurrent Unit (Conv2D-GRU) model is built to fully extract steep changes information from inputs and achieve day-ahead load forecasting. Naive persistence, Auto regressive (AR), Gradient boosting decision trees (GBDT), Convolutional neural network (CNN), Long short-term memory (LSTM) and Gate recurrent unit (GRU) are used for comparison. Compared to Naive persistence, the Conv2D-GRU-SC resulted in a decrease of 54.08 % in Mean absolute error (MAE), a decrease of 57.58 % in Root mean square error (RMSE) and an increase of 51.31 % in the R-Square (R2).

The COVID-19 pandemic's impact on user consumption: A shift towards very efficient load forecasting

The COVID-19 pandemic has prompted significant shifts in energy consumption patterns, necessitating a departure from traditional data analysis methods. Accurate energy forecasting models play a crucial role in various domains of planning and energy management. It is possible to predict the demand for electricity consumption using a variety of techniques. Fundamentally, this research comprises two main components. The first phase assesses the influence of the COVID-19 pandemic on user consumption patterns through time series analysis. The subsequent segment is dedicated to long-term load forecasting, employing both statistical methods termed as Holt-Winters and Prophet algorithms and an Artificial Neural Network approach known as Long Short-Term Memory. These approaches aim to predict electricity demand amid the diverse and intricate context of the COVID-19 pandemic. It utilizes both conventional time series forecasting methods and advanced Artificial Intelligence models to enhance the accuracy of load forecasting. Since rapid societal and economic shifts are unpredictable during a pandemic, methods based on Artificial Intelligence are becoming extremely important due to its high precision. Though, it is problematic to conduct load prediction with high accuracy because of influencing factors like climatic, socioeconomic, and seasonal aspects. Finally, using hourly-driven power consumption data from Houston, Texas, USA, the Long Short-Term Memory model outperforms the Holt-Winters and Prophet models in a test of generalizability and accuracy. It is possible to verify the accuracy of experimental findings by analyzing the resulting constraints and influential factors.

Real-Time Load Forecasting and Adaptive Control in Smart Grids Using a Hybrid Neuro-Fuzzy Approach

The transition to smart grids is revolutionizing the management and distribution of electrical energy. Nowadays, power systems must precisely estimate real-time loads and use adaptive regulation to operate in the era of sustainable energy. To address these issues, this paper presents a new approach—a hybrid neuro-fuzzy system—that combines neural networks with fuzzy logic. We use neural networks’ adaptability to describe complex load patterns and fuzzy logic’s interpretability to fine-tune control techniques in our approach. Our improved load forecasting system can now respond to changes in real-time due to the combination of these two powerful methodologies. Developing, training, and implementing the forecasting and control system are detailed in this article, which also explores the theoretical underpinnings of our hybrid neuro-fuzzy approach. We demonstrate how the technology improves grid stability and the accuracy of load forecasts by using adaptive control methods. Furthermore, comprehensive simulations confirm the proposed technology, showcasing its smooth integration with smart grid infrastructure. Better energy management is just the beginning of what our method can accomplish; it also paves the way for a more sustainable energy future that is easier on the planet and its inhabitants. In conclusion, this study’s innovative approach to adaptive control and real-time load forecasting advances smart grid technology, which, in turn, improves sustainability and energy efficiency.

A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies

Behind the meter (BTM) distributed energy resources (DERs), such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicle (EV) charging infrastructures, have experienced significant growth in residential locations. Accurate load forecasting is crucial for the efficient operation and management of these resources. This paper presents a comprehensive survey of the state-of-the-art technologies and models employed in the load forecasting process of BTM DERs in recent years. The review covers a wide range of models, from traditional approaches to machine learning (ML) algorithms, discussing their applicability. A rigorous validation process is essential to ensure the model’s precision and reliability. Cross-validation techniques can be utilized to reduce overfitting risks, while using multiple evaluation metrics offers a comprehensive assessment of the model’s predictive capabilities. Comparing the model’s predictions with real-world data helps identify areas for improvement and further refinement. Additionally, the U.S. Energy Information Administration (EIA) has recently announced its plan to collect electricity consumption data from identified U.S.-based crypto mining companies, which can exhibit abnormal energy consumption patterns due to rapid fluctuations. Hence, some real-world case studies have been presented that focus on irregular energy consumption patterns in residential buildings equipped with BTM DERs. These abnormal activities underscore the importance of implementing robust anomaly detection techniques to identify and address such deviations from typical energy usage profiles. Thus, our proposed framework, presented in residential buildings equipped with BTM DERs, considering smart meters (SMs). Finally, a thorough exploration of potential challenges and emerging models based on artificial intelligence (AI) and large language models (LLMs) is suggested as a promising approach.

Peak shaving at system level with a large district heating substation using deep learning forecasting models

The decarbonisation of urban district heating (DH) systems requires increased heating grid flexibility. Therefore, this article examines the optimised operation of a tank thermal energy storage (TTES) on the secondary side of a new DH substation for an industrial site in a German city, in order to shave the peaks of the whole DH system and thus reduce the need for heat-only boilers (HOB). The accuracy of heat load and return temperature forecasts for both the industrial consumer and the DH grid is critical to the performance of the optimisation-based operating strategy of the TTES. Therefore, long short-term memory neural networks are used in combination with continuous model updates through incremental learning to create two forecasting scenarios, one using only preceding data for the forecasts and the other including future weather data. The results show that high forecasting accuracy is most relevant for reducing the annual maximum peak, with a reduction of 2.8% in the preceding data scenario, 4% with future weather data and 7% in a benchmark with perfect forecasts. The economic viability of the storage through HOB heat savings is primarily affected by lower forecasting accuracy when the additional cost of HOB heat is less than 60 €/MWh.

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.

Building heating load forecasting based on the theory of transient heat transfer and deep learning

Reliable and accurate heating load forecasting can provide comprehensive information for the monitoring and control of Heating, Ventilation, and Air Conditioning (HVAC) in buildings, which reduce uncertainty on the load demand of energy effectively. Data-driven method has demonstrated a potential to forecast heating load, but current methods generally lack the consideration of features on model forecasting performance and applicability. Based on the theory of transient heat transfer and conduction transfer function for building hourly heating load calculation, this study proposes an idea that comprehensively selects physical variables affecting the hourly dynamic changes of the building heating load as features, which enhances the persuasiveness of selecting datasets features. Then we use the deep learning method to build short-term heating load forecasting models, and explore the influence of different feature combinations and time steps on the model forecasting performance. Combining the physical property of building heat gains, we divide the features into external heat gains, internal heat gains and historical heating load and then show their performance of models at different time steps. It is shown that the two-hour data of external heat gains and internal heat gains, the three-hour data of past heat load have the highest value. The best model can achieve a mean absolute error of 0.153 kW and a mean absolute percentage error of 6.2 %. These findings are helpful for researchers to select more relevant features to forecast heating load in buildings and improve their models through physical aspect.

Improved Autuoformer Electricity load forecasting based on model fusion

The development of the power system is undergoing rapid changes. The future direction of power system development is inevitably smart grids. This paper proposes an optimization of the Autoformer model based on model fusion to improve the accuracy of electricity load forecasting. By incorporating the predictive results of traditional statistical multivariate linear regression (MLR) and machine learning method LightGBM into the Autoformer model, we aim to fully leverage the strengths of both traditional statistical and machine learning approaches to enhance the predictive accuracy of the Autoformer model. Experimental results, using mean MAE and MSE as the evaluation metrics, demonstrate that the algorithm proposed in this paper exhibits better performance in the prediction results.

Optimization research on short-term load forecasting method for electric vehicles based on SSA-SVM

In order to improve the accuracy of short-term electric vehicle load forecasting, a combined forecasting model based on Sparrow Search Algorithm (SSA) and Support Vector Machine (SVM) is constructed. Using SVM model to predict electric vehicle load, accelerating the convergence speed of the prediction model through sparrow search algorithm, conducting global optimization in the solution space, expressing the impact of calculated parameters on the model through fitness, searching for the optimal model parameter data, and improving the overall accuracy of the prediction model. Through simulation experiments, comparing the prediction results of SVM models, and SSA-SVM shows better performance in electric vehicle load prediction.