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To handle the data imbalance and inaccurate prediction in power load forecasting, an integrated data denoising power load forecasting method is designed. This method divides data into administrative regions, industries, and load characteristics using a four-step method, extracts periodic features using Fourier transform, and uses Kmeans++ for clustering processing. On this basis, a Transformer model based on an adversarial adaptive mechanism is designed, which aligns the data distribution of the source domain and target domain through a domain discriminator and feature extractor, thereby reducing the impact of domain offset on prediction accuracy. The mean square error of the Fourier transform clustering method used in this study was 0.154, which was lower than other methods and had a better data denoising effect. In load forecasting, the mean square errors of the model in predicting long-term load, short-term load, and real-time load were 0.026, 0.107, and 0.107, respectively, all lower than the values of other comparative models. Therefore, the load forecasting model designed for research has accuracy and stability, and it can provide a foundation for the precise control of urban power systems. The contributions of this study include improving the accuracy and stability of the load forecasting model, which provides the basis for the precise control of urban power systems. The model tracks periodicity, short-term load stochasticity, and high-frequency fluctuations in long-term loads well, and possesses high accuracy in short-term, long-term, and real-time load forecasting. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Accurate electricity load forecasting is crucial for efficient energy management, allowing utility companies to optimize resource allocation, reduce operational costs, and ensure a reliable power supply. Various factors, such as weather conditions, economic activities, and demographic changes, significantly influence electricity load. To address these challenges, we designed a deep learning Bidirectional Long Short-Term Memory (BiLTSM-based model) for electricity load forecasting, leveraging weather data as the primary feature and signal decomposition as an additional feature. To select the best weather parameters, we utilize the Principal Component Analysis (PCA) and Correlation Coefficient (CC) to choose the most correlated weather parameters as inputs for the model. Moreover, we introduce spatial correlation to select the best weather locations. Additionally, we perform a signal decomposition technique using the Ensemble Empirical Mode Decomposition (EEMD) to decompose the historical load signal into Intrinsic Mode Functions (IMFs). We also investigate procedures to choose the best IMF to give the best accuracy. As a case study, we investigate the electricity load data in the Jakarta-Banten region of Indonesia, known for its high consumer and industrial activity. The results demonstrate that combining the BiLSTM model and EEMD achieves high prediction accuracy, with a Correlation Coefficient (CC) of 0.956 and a Mean Absolute Percentage Error (MAPE) of 2.824%. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the Similar Days Method (SDM) and Artificial Neural Network (ANN), introduces several novelties: (1) selection of similar days based on hidden representations of day data using Autoencoder (AE); (2) enhancement of model generalization by utilizing a broader set of training examples; (3) incorporating the relative importance of training examples derived from the similarity measure during training; and (4) mitigation of the influence of outliers by applying an ensemble of ANN models trained with different data splits. The presented AE configuration and procedure for selecting similar days generated a higher-quality training dataset, which led to more robust predictions by the ANN model for days with unexpected deviations. Experiments were conducted on actual load data from a Serbian electrical power system, and the results were compared to predictions obtained by the field-proven STLF tool. The experiments demonstrated an improved performance of the presented solution on test days when the existing STLF tool had poor predictions over the past year. [ABSTRACT FROM AUTHOR]

Accurate electric load forecasting at various time periods is considered a necessary challenge for electricity consumers and generators to maximize their economic efficiency in energy markets. Hence, the accuracy and effectiveness of existing electric load forecasting approaches depends on the data quality. Nowadays, with the implementation of modern power systems and Internet of Things technology, forecasting models are faced with a large volume of data, which puts the security and health of data at risk due to the use of numerous measuring devices and the threat of cyber‐attackers. In this study, a cyber‐resilient hybrid deep learning‐based model is developed that accurately forecasts electric load in short‐term and long‐term time horizons. The architecture of the proposed model systematically integrates stacked multilayer denoising autoencoder (SMDAE) and generative adversarial network (GAN) and is called SMDAE‐GAN. In the proposed framework, SMDAE layer is used to pre‐process and remove real fs and intentional anomalies in data, and GAN layer is also utilized to forecast electric load values. The effectiveness of the SMDAE‐GAN structure is studied using realistic electrical load data monitored in the distribution network of Tabriz, Iran, and meteorological data measured in weather station there. Compared with other conventional load forecasting approaches, the developed framework has the highest accuracy in both cases of using normal data with real‐world noise and damaged data under false data injection attacks. [ABSTRACT FROM AUTHOR]

To ensure the constant availability of electrical energy, power companies must consistently maintain a balance between supply and demand. However, electrical load is influenced by a variety of factors, necessitating the development of robust forecasting models. This study seeks to enhance electricity load forecasting by proposing a hybrid model that combines Sorted Coefficient Wavelet Decomposition with Long Short-Term Memory (LSTM) networks. This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. Various models, including Stacked LSTM, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), were compared and optimized using grid search with cross-validation on consumption data from Lome, a city in Togo. The results indicate that the ConvLSTM model outperforms its counterparts based on Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and correlation coefficient (R2) metrics. The ConvLSTM model was further refined using wavelet decomposition with coefficient sorting, resulting in the WT+ConvLSTM model. This proposed approach significantly narrows the gap between actual and predicted loads, reducing discrepancies from 10–50 MW to 0.5–3 MW. In comparison, the WT+ConvLSTM model surpasses Autoregressive Integrated Moving Average (ARIMA) models and Multilayer Perceptron (MLP) type artificial neural networks, achieving a MAPE of 0.485%, an RMSE of 0.61 MW, and an R2 of 0.99. This approach demonstrates substantial robustness in electricity load forecasting, aiding stakeholders in the energy sector to make more informed decisions. [ABSTRACT FROM AUTHOR]

In the context of the electricity sector's liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However, their effectiveness is hampered by the variable temporal characteristics of real-time electricity prices and a wide array of influencing factors. These challenges hinder a single model's ability to discern the regularity, thereby compromising forecast precision. This study introduces a novel hybrid system to enhance forecast accuracy. Firstly, by employing an advanced decomposition technique, this methodology identifies different variation features within the electricity price series, thus bolstering feature extraction efficiency. Secondly, the incorporation of a novel multi-objective intelligent optimization algorithm, which utilizes two objective functions to constrain estimation errors, facilitates the optimal integration of multiple deep learning models. The case study uses electricity market data from Australia and Singapore to validate the effectiveness of the algorithm. The forecast results indicate that the hybrid short-term electricity price forecasting system proposed in this paper exhibits higher prediction accuracy compared to traditional single-model predictions, with MAE values of 7.3363 and 4.2784, respectively. [ABSTRACT FROM AUTHOR]

This study presents short-term electricity load forecasting for the New England area by processing initial data through correlation assessment and data clustering. This method is combined with artificial neural networks to improve accuracy and forecast performance. Data preprocessing focuses on two main issues: identifying correlations between variables to eliminate less relevant factors and retaining highly correlated variables to reduce noise, as well as reducing the data sample size before training the neural network. This evaluation aims to determine the factors that have a significant impact on electricity load. These factors can include previous load values, weather conditions, time, types of electricity usage, and others. This technique ensures that reducing the size in both dimensions of the large dataset does not result in the loss of critical information, maintaining the accuracy of computational programs and the performance of neural network training at high levels. The neural network is trained to classify and cluster data based on previously identified correlated characteristics. As a result, the forecasting model can make more accurate predictions about future electricity loads. Experimental results show that the proposed method achieved more than 97 % accuracy, outperforming traditional methods in both speed and load forecasting accuracy. The new dataset had 63% fewer samples compared to the initial dataset. [ABSTRACT FROM AUTHOR]

Electric load forecasting plays a vital role in all aspects of the electrical system, including generation, transmission, distribution, and electricity retail. The LightGBM ensemble learning method has been widely applied in load forecasting and has yielded many positive results. This study presents an algorithm combining the grid space of hyperparameters with cross-validation to evaluate the accuracy of LightGBM models across different hyperparameter values. Peak load data from Ho Chi Minh City were used to enhance the reliability of the results. Analysis of the results based on boxplot statistical charts indicated that the accuracy of the LightGBM model significantly depends on the hyperparameter values. Moreover, using default hyperparameter values may result in large errors in load forecasting. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

This research work focuses on addressing the challenges of electric load forecasting through the combination of Support Vector Regression and Long Short-Term Memory (SVR/LSTM) methodology. The model has been modified by a flexible version of the Gorilla Troops optimization algorithm. The objective of this study is to enhance the precision and effectiveness of load forecasting models by integrating the adaptive functionalities of the Gorilla Troops algorithm within the SVR/LSTM framework. To assess the efficacy of the proposed methodology, a comprehensive series of experiments and evaluations have been undertaken, utilizing authentic data obtained from 200 residential properties located in Texas, United States of America. The dataset comprises historical records of electricity consumption, meteorological data, and other pertinent variables that exert an impact on energy demand. The presence of this general dataset enhances the dependability and inclusiveness of the empirical findings. The proposed methodology was evaluated against various contemporary load forecasting techniques that are widely employed in the industry. The results of a comprehensive evaluation and performance analysis indicate that the modified SVR/LSTM model exhibits superior performance compared to the existing methods in terms of accuracy and robustness. The comparison results unequivocally demonstrate the superiority of the proposed method in accurately forecasting electric load demand. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544). [ABSTRACT FROM AUTHOR]

The integration of stochastic and intermittent distributed PVs brings great challenges for power system operation. Precise net load forecasting performs a critical factor in dependable operation and dispensing. An approach to probabilistic net load prediction is introduced for sparse variant Gaussian process based algorithms. The forecasting of the net load is transferred to a regression problem and solved by the sparse variational Gaussian process (SVPG) method to provide uncertainty quantification results. The proposed method can capture the uncertainties caused by the customer and PVs and provide effective inductive reasoning. The results obtained using real-world data show that the proposed method outperforms other best-of-breed algorithms. [ABSTRACT FROM AUTHOR]

With the global objectives of achieving a "carbon peak" and "carbon neutrality" along with the implementation of carbon reduction policies, China's industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leading to data gaps. Furthermore, in the domain of aggregated load forecasting, considering the potential interactions among aggregated loads can help market participants engage in cross-market transactions. However, aggregated loads often lack clear geographical locations, making it difficult to predefine graph structures. To address the issue of data quality, this study proposes a model named adversarial graph convolutional imputation network (AGCIN), combined with local and global correlations for imputation. To tackle the problem of the difficulty in predefining graph structures for aggregated loads, this study proposes a learnable adjacency matrix, which generates an adaptive adjacency matrix based on the relationships between different sequences without the need for geographical information. The experimental results demonstrate that the proposed imputation method outperforms other imputation methods in scenarios with random and continuous missing data. Additionally, the prediction accuracy of the proposed method exceeds that of several baseline methods, affirming the effectiveness of our approach in imputation and prediction, ultimately enhancing the accuracy of aggregated load forecasting. [ABSTRACT FROM AUTHOR]

A large-scale integration of photovoltaic (PV) systems can degrade the stability of the power grid. Therefore, it is important to accurately predict the short-term output power generated from PV systems to achieve better grid power distribution and allocation. For this reason, a short-term PV power prediction model that uses the data collected from temperature sensors, irradiance sensors, and other relevant sensors was proposed, in which an improved dragonfly algorithm (IDA) was applied to optimize the support vector machine (SVM). First, the output power curves of PV systems under sunny, cloudy, and rainy conditions were analyzed to determine the input variables of the prediction model, which included temperature, relative humidity, and solar radiation intensity. Second, the original dragonfly algorithm in the optimization process was improved, and then, this IDA was utilized to optimize the parameters of SVM, enhancing the predictive capability of the model. Finally, the IDA-optimized SVM (IDA-SVM) model was applied to predict the PV output power. Test performance results demonstrated that the average absolute percentage errors of IDA-SVM were 2.42, 5.96, and 7.44% for sunny, cloudy, and rainy days, respectively, outperforming other comparative models. The performance results showed that the proposed model can not only improve the stability of PV integration, but also effectively increase the penetration rate of PV energy and enhance the reliability of power system operation. [ABSTRACT FROM AUTHOR]

Due to changes in urban residents' consumption habits and lifestyles, accurately predicting natural gas consumption has become increasingly important. To address this issue, this paper proposes a forecasting model that combines Ensemble Learning (EL), Variational Mode Decomposition (VMD), Transformer, and LSTM. First, XGBoost, CatBoost, and LightGBM are used as base learners in the ensemble learning framework, with the predictions generated by the ensemble model integrated into the original dataset. Next, the VMD method is employed to decompose the natural gas load sequence into several intrinsic mode functions (IMFs), effectively extracting the inherent features of the natural gas load sequence. Finally, the data is input into the Transformer-ResLSTM network for prediction. This network replaces the original Transformer decoder structure with an LSTM network and fully connected layers, creating a new decoder structure. Additionally, a residual connection mechanism is introduced in both the encoder of the Transformer network and the new decoder structure. Experimental results show that, compared to traditional models such as ARIMA, Transformer, GRU, and LSTM, the proposed hybrid model significantly improves prediction accuracy, reducing MSE by 92–98% and MAE by 74–83%. In summary, this method demonstrates significant potential and practical value in enhancing the accuracy of natural gas load forecasting. [ABSTRACT FROM AUTHOR]

To improve the current source-grid, load-storage microgrid coordinated optimal scheduling method, which is not ideal in terms of efficiency and effectiveness, the study combines convolutional neural network, variational modal decomposition, and long and short-term memory neural network to realize the short-term prediction of microgrid electric load. Based on this, a mathematical model having source-grid, load-storage coordinated optimal scheduling and an improved particle swarm algorithm are proposed for it. Compared with the particle swarm backpropagation model, the proposed microgrid power load short-term prediction model reduces the average absolute percentage error and root mean square error by 0.38% and 39.5%, respectively. In addition, the economic cost of the proposed power grid coordination and optimization scheduling model based on improved particle swarm optimization algorithm (IPSO) is lower, at $3954.3, and the load fluctuation is less, at 56.6 W. This indicates that the model proposed by the research institute helps to achieve self-sufficiency of electricity within the microgrid and mutual assistance between microgrids, thereby tapping into scheduling potential, and also helps to achieve economic electricity scheduling strategies, avoiding unnecessary thermal power generation and carbon dioxide emissions, and improving reliability. Therefore, the scheme proposed in the study can effectively realize the coordinated and optimal dispatch of source-network load and storage beneficial to the power enterprises. [ABSTRACT FROM AUTHOR]

The accurate short-term point and probabilistic load forecasts are critically important for efficient operation of power systems and electricity bargain in the market. Fuzzy systems achieved limited success in electric load forecasting. On the other hand, support vector regression models have seldom been part of a winning solution of the electric load forecasting competitions during the last decade. In this paper, we propose a methodology to integrate the fuzzy memberships with support vector regression (SVR) and support vector quantile regression (SVQR) models for short-term point and probabilistic load forecasting, respectively. One fuzzy membership function is proposed to efficiently calculate the relative importance of the observations in the load history. Three SVR and one SVQR models, including L1-norm based SVR, L2-norm based SVR, kernel-free quadratic surface SVR and SVQR models, are utilized to demonstrate the effectiveness of the proposed methodology. For point load forecasting, we compare the proposed fuzzy SVR models with a multiple linear regression, a feed-forward neural network, a fuzzy interaction regression, and four SVR models. For probabilistic load forecasting, the proposed fuzzy SVQR model is compared with a quantile regression model, a quantile regression neural network, and a SVQR model. The results on the data of global energy forecasting competition 2012, demonstrate that the proposed fuzzy component can improve the underlying SVR and SVQR models to outperform their counterparts and commonly-used models for point and probabilistic load forecasting, respectively. [ABSTRACT FROM AUTHOR]

Dynamic load forecasting is essential for effective energy management and grid operation. The use of GRU (Gated Recurrent Unit) and Long Short-Term Memory (LSTM) networks for precise load prediction is investigated in this paper. This research examines dynamic load patterns by innovatively integrating heterogeneous information from several datasets. The results show that the LSTM and GRU models are equally good at making predictions and that this holds true across a variety of datasets. Furthermore, the models' ability to accurately capture the temporal relationships in the load data is demonstrated by their low Mean Absolute Percentage Error (MAPE) and Mean Squared Error (MSE) values. Additionally, the comparative analysis results, which highlight flexibility in model selection, can aid energy sector decision makers. The significance of precise load projections for maintaining grid dependability and optimizing resources is further highlighted by this work, which also elucidates the effects of forecast inaccuracies on decision-making procedures. Our research study provides important information for power system management strategy planning, which in turn promotes the continuous innovation of smart grids in dynamic load forecasting to keep up with changing energy consumption patterns. [ABSTRACT FROM AUTHOR]

Short-term electricity load forecasting provides a basis for day-ahead energy scheduling. To improve the accuracy of short-term electricity load forecasts and deeply explore the temporal characteristics of load sequences, a method is proposed to extract predictable components of load sequences based on the secretary bird optimization algorithm (SBOA)-optimized successive variational mode decomposition (SVMD). This method decomposes the electricity load sequence into multiple subsequences under different time series. The combined forecasting architecture of the temporal convolutional network (TCN) and the bidirectional long short-term memory network (BiLSTM) is introduced to mine the temporal characteristics of each load component, resulting in short-term load forecasting outcomes. A case study is conducted using the annual electricity load data for the year 2018 from a specific region in Belgium. The experimental results show that the mean absolute error (MAE) of the TCN–BiLSTM model is reduced by 47.8%, 32.8%, and 11.5%, respectively, compared to other models. The root mean square error (RMSE) is reduced by 42.9%, 39.2%, and 11.3%, respectively, and the average goodness of fit R2 is reduced by 9.81%. [ABSTRACT FROM AUTHOR]

With the increasing a huge amount of end users using electricity in modern cities, smart grids have some critical problems for energy efficiency and managing renewable energy resources. Therefore, electricity load forecasting is an important strategy to avoid power disconnection and power communication damages in smart grids. On the other hand, the Internet of Things (IoT) devices can collect appropriate data from end users with smart metering. In addition, smart devices can gather critical data from power stations using smart sensors and actuators to provide the quality of service (QoS) factors and energy efficiency of electricity transmission in the smart girds. Due to the huge amount of data transmission, machine learning is useful for evaluating electricity load forecasting and power stability in smart grids. For detecting electric load forecasting and energy consumption factors, this paper develops Bayesian optimization for K-nearest neighbor (BOKNN) with the hyper-parameters function to detect the electricity load forecasting and estimated power consumption factors in smart grids. The proposed technique optimizes the performance of electricity load forecasting and enhances the structure of the machine learning efficiency with high accuracy. Two real-data sets as our case studies for short-term electricity load forecasting are applied to evaluate the proposed BOKNN as existing experimental testbeds. The simulation results illustrate that the proposed BOKNN provides optimized high accuracy with 88.33% and 98.13% of correlation coefficient and minimum mean absolute error with 0.04% for existing datasets. The experimental results show that the suggested BOKNN model greatly outperforms other prediction algorithms. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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The stability of smart grids is crucial for ensuring reliable and efficient power distribution in modern energy systems. This paper presents an optimized Long Short-Term Memory model for predicting smart grid stability, leveraging the Novel Guide-Waterwheel Plant Algorithm (Guide-WWPA) for enhanced performance. Traditional methods often struggle with the complexity and dynamic nature of smart grids, necessitating advanced approaches for accurate predictions. The proposed LSTM model, optimized using Guide-WWPA, addresses these challenges by effectively capturing temporal dependencies and nonlinear relationships in the data. The proposed approach involves a comprehensive preprocessing pipeline to handle data heterogeneity and noise, followed by the implementation of the LSTM model optimized through Guide-WWPA. The Guide-WWPA combines the strength of the WWPA with a novel guidance mechanism, ensuring efficient exploration and exploitation of the search space. The optimized LSTM is evaluated on a real-world smart grid dataset, demonstrating superior performance compared to traditional optimization techniques. Experimental Results indicate significant improvements in prediction accuracy and computational efficiency, highlighting the potential of the Guide-WWPA optimized LSTM for real-time smart grid stability prediction. This work contributes to the development of intelligent energy management systems, offering a robust tool for maintaining grid stability and enhancing overall energy reliability. On the other hand, statistical evaluations were carried out to prove the stability and difference of the proposed methodology. The results of the experiments demonstrate that the Guide-WWPA + LSTM strategy is superior to the other machine learning approaches. [ABSTRACT FROM AUTHOR]

Medium–long-term photovoltaic (PV) output forecasting is of great significance to power grid planning, power market transactions, power dispatching operations, equipment maintenance and overhaul. However, PV output fluctuates greatly due to weather changes. Furthermore, it is frequently challenging to ensure the accuracy of forecasts for medium–long-term forecasting involving a long time span. In response to the above problems, this paper proposes a medium–long-term forecasting method for PV output based on amplitude-aware permutation entropy component reconstruction and the graph attention network. Firstly, the PV output sequence data are decomposed by ensemble empirical mode decomposition (EEMD), and the decomposed intrinsic mode function (IMF) subsequences are combined and reconstructed according to the amplitude-aware permutation entropy. Secondly, the graph node feature sequence is constructed from the reconstructed subsequences, and the mutual information of the node feature sequence is calculated to obtain the graph node adjacency matrix which is applied to generate a graph sequence. Thirdly, the graph attention network is utilized to forecast the graph sequence and separate the PV output forecasting results. Finally, an actual measurement system is used to experimentally verify the proposed method, and the outcomes indicate that the proposed method, which has certain promotion value, can improve the accuracy of medium–long-term forecasting of PV output. [ABSTRACT FROM AUTHOR]

The power dispatching network forms the backbone of efforts to automate and modernize power grid dispatching, rendering it an indispensable infrastructure element within the power system. However, accurately forecasting future flows remains a formidable challenge due to the network's intricate nature, variability, and extended periods of missing data resulting from equipment maintenance and anomalies. Vital to enhancing prediction precision is the interpolation of missing values aligned with the data distribution across other time points, facilitating the effective capture of nonlinear patterns within historical flow sequences. To address this, we propose a transfer learning approach leveraging the gated recurrent unit (GRU) for interpolating missing values within the power dispatching network's flow sequence. Subsequently, we decompose the generation of future flow predictions into two stages: first, extracting historical features using the GRU, and then generating robust predictions via eXtreme Gradient Boosting (XGBoost). This integrated process termed the GRU-XGBoost module, is applied in experiments on four flow sequences obtained from a power grid company in southern China. Our experimental findings illustrate that the proposed flow prediction model outperforms both machine learning and neural network models, underscoring its superiority in short-term flow prediction for power-dispatching networks. [ABSTRACT FROM AUTHOR]

The current power load exhibits strong nonlinear and stochastic characteristics, increasing the difficulty of short-term prediction. To more accurately capture data features and enhance prediction accuracy and generalization ability, in this paper, we propose an efficient approach for short-term electric load forecasting that is grounded in a synergistic strategy of feature optimization and hyperparameter tuning. Firstly, a dynamic adjustment strategy based on the rate of the change of historical optimal values is introduced to enhance the PID-based Search Algorithm (PSA), enabling the real-time adjustment and optimization of the search process. Subsequently, the proposed Improved Population-based Search Algorithm (IPSA) is employed to achieve the optimal adaptive variational mode decomposition of the load sequence, thereby reducing data volatility. Next, for each load component, a Bi-directional Gated Recurrent Unit network with an attention mechanism (BiGRU-Attention) is established. By leveraging the interdependence between feature selection and hyperparameter optimization, we propose a synergistic optimization strategy based on the Improved Population-based Search Algorithm (IPSA). This approach ensures that the input features and hyperparameters for each component's predictive model achieve an optimal combination, thereby enhancing prediction performance. Finally, the optimal parameter prediction model is used for multi-step rolling forecasting, with the final prediction values obtained through superposition and reconstruction. The case study results indicate that this method can achieve an adaptive optimization of hybrid prediction model parameters, providing superior prediction accuracy compared to the commonly used methods. Additionally, the method demonstrates robust adaptability to load forecasting across various day types and seasons. Consequently, this approach enhances the accuracy of short-term load forecasting, thereby supporting more efficient power scheduling and resource allocation. [ABSTRACT FROM AUTHOR]

Accurate and reliable medium- and long-term load forecasting is crucial for the rational planning and operation of power systems. However, existing methods often struggle to accurately extract and capture long-term dependencies in load data, leading to poor predictive accuracy. Therefore, this paper proposes a medium- and long-term residential load forecasting method based on FEDformer, aiming to capture long-term temporal dependencies of load data in the frequency domain while considering factors such as electricity prices and temperature, ultimately improving the accuracy of medium- and long-term load forecasting. The proposed model employs Discrete Cosine Transform (DCT) for frequency domain transformation of time-series data to address the Gibbs phenomenon caused by the use of Discrete Fourier Transform (DFT) in FEDformer. Additionally, causal convolution and attention mechanisms are applied in the frequency domain to enhance the model's capability to capture long-term dependencies. The model is evaluated using real-world load data from power systems, and experimental results demonstrate that the proposed model effectively learns the temporal and nonlinear characteristics of load data. Compared to other baseline models, DCTformer improves prediction accuracy by 37.5% in terms of MSE, 26.9% in terms of MAE, and 26.24% in terms of RMSE. [ABSTRACT FROM AUTHOR]

A CNN-BIGRU-AT model for power load forecasting is constructed by introducing Attention Mechanism (AT) into a model combining Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BIGRU), which has high prediction accuracy. In this model, CNN is first used to extract relevant features from power load data, and then BIGRU is applied to capture the inherent complex nonlinear dynamic patterns of power load. Additionally, Attention Mechanism (AT) is incorporated to further refine the feature extraction of power load data. The North Grey Wolf Optimizer Algorithm (NGO) is used to optimize parameters such as the number of hidden layer nodes, initial learning rate, and regularization coefficient in BIGRU, thus obtaining the modeling method of NGO-CNN-BIGRU-AT. Finally, the validity and feasibility of the model are substantiated using actual power load data from a certain region in China. The results demonstrate that the model has better performance than conventional models, including BP, BIGRU, CNN-BIGRU, and CNN-BIGRU-AT. [ABSTRACT FROM AUTHOR]

To accommodate the rapid development of the distribution network of China, it is essential to research load forecasting methods with higher accuracy and stronger generalization capabilities in order to optimize distribution system control strategies, ensure the efficient and reliable operation of the power system, and provide a stable power supply to users. In this paper, a short-term load forecasting method is proposed for low-voltage distribution substations based on the bidirectional long short-term memory (BiLSTM) model. First, principal component analysis (PCA) and the fuzzy C-means method based on a genetic algorithm (GA-FCM) are used to extract the main influencing factors and classify different types of user electricity consumption behaviors. Then, the BiLSTM forecasting model utilizing the stochastic weight averaging (SWA) algorithm to enhance generalization capability is constructed. Finally, the load data from a low-voltage distribution substation in China over recent years are selected as a case study. Compared with conventional LSTM and BiLSTM prediction models, the annual electricity load curves for various user types forecasted by the PCA-BiLSTM model are more closely aligned with actual data curves. The proposed BiLSTM forecasting model exhibits higher accuracy and can forecast user electricity consumption data that more accurately reflect real-life usage. [ABSTRACT FROM AUTHOR]

In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market. [ABSTRACT FROM AUTHOR]

For the air conditioning with secondary pump variable flow system, considering the regional cooling situation, the multi-objective regression method was used to solve the load forecasting problem for improving the accuracy of load forecasting. For the central air conditioning, two multi-objective regression load forecasting models of multi-objective support vector regression(SVR) and multi-objective long short-term memory(LSTM)neural network were proposed. The two models were used to train and predict on the data of the secondary pump variable flow system of the hospital in Shanghai, and the results were compared with those of the single objective regression prediction model. The results show that the prediction accuracies of the two multi-objective prediction models are higher than that of the single objective regression prediction model, and the multi-objective SVR load forecasting model has higher prediction accuracy than the multi-objective LSTM load forecasting model. [ABSTRACT FROM AUTHOR]

This study presents two innovative machine learning-based models: one for daily electrical load forecasting in the State of Rio de Janeiro and another for monthly forecasting for each Light concessionaire substation in the Metropolitan Area of Rio de Janeiro (MARJ). The utilized data include (1) daily electrical load data from the National System Operator (ONS) for the State of Rio de Janeiro spanning four years (2017-2020); (2) monthly electrical load data from 84 Light substations over 11 years (2010-2020); and (3) maximum, minimum, and mean air temperatures. Additionally, remotely sensed land-surface temperature (LST) based on Landsat data from 1984 to 2020 is employed to reconfigure the existing meteorological network in MARJ based on the distribution of Light substations. The regressive machine learning algorithms undertook training using a cross-validation procedure in 500 daily and 500 monthly training-testing experiments. Results for daily-ONS and monthly-Light loads show average correlations (hindcast in parentheses) of the fitted models of 0.85±0.09 (0.83±0.07) and 0.89±0.05 (0.91±0.06), respectively. The model's Mean Absolute of Error (MAE) values correspond to a percentage error of about 4.03% (daily) and 4.83% (monthly). According to the monthly electrical load behavior revealed, when the temperature changes from 23 to 26℃ at MARJ, it rises roughly from 1.92 x 106 ± 67227.4 kWh to 2.70 x 106 ± 90198.5 kWh. A cluster analysis was conducted to optimize the meteorological station network, considering the locations of the existing 18 meteorological stations, the 84 Light electrical load substations, and the urban heat island cores. This analysis identified seven specific locations for installing new meteorological stations, aiming to improve the spatial resolution of electrical load modeling in MARJ. This insight opens paths for future research to refine the network and further enhance the models' precision in capturing the intricate relationships between meteorological factors and electrical loads in the region. [ABSTRACT FROM AUTHOR]

Short‐term load forecasting is critical for power system planning and operations, and ensemble forecasting methods for electricity loads have been shown to be effective in obtaining accurate forecasts. However, the weights in ensemble prediction models are usually preset based on the overall performance after training, which prevents the model from adapting in the face of different scenarios, limiting the improvement of prediction performance. In order to improve the accurateness and validity of the ensemble prediction method further, this paper proposes an ensemble deep reinforcement learning approach using Q‐learning dynamic weight assignment to consider local behaviours caused by changes in the external environment. Firstly, the variational mode decomposition is used to reduce the non‐stationarity of the original data by decomposing the load sequence. Then, the recurrent neural network (RNN), long short‐term memory (LSTM), and gated recurrent unit (GRU) are selected as the basic power load predictors. Finally, the optimal weights are ensembled for the three sub‐predictors by the optimal weights generated using the Q‐learning algorithm, and the final results are obtained by combining their respective predictions. The results show that the forecasting capability of the proposed method outperforms all sub‐models and several baseline ensemble forecasting methods. [ABSTRACT FROM AUTHOR]

Load forecasting is a key aspect of power system design, operation, and maintenance. To obtain an accurate forecast model which is very vital for the development of any power sector capacity expansion plan be it on generation, transmission and distribution, regression (0.0139) and mean square error (0.99791) were used as criteria for selecting the optimal model for load forecasting. The higher the value of regression, the better the value of the accuracy of the load forecast. In the same vein, the lower the value of the mean square error, the better the accuracy of the load forecast. The load forecast using Backpropagation artificial neural network (BPANN) obtained load forecast in the year 2035 to be 85998MW. This forecast is necessary to provide an increase in generation capacity to meet 85836.99MW of load by 2035. Taking 2021 as base year, the total load demand, which comprises the industrial, commercial, and residential customers were estimated and used to forecast the total load requirements of the customers in year 2035. The ten experimental results obtained by creating ten different topologies of BPANN validated the choice of 30 hidden layer neurons for use in the load forecasting. [ABSTRACT FROM AUTHOR]

An improved fuzzy time series algorithm based on clustering is designed in this paper. The algorithm is successfully applied to short-term load forecasting in the distribution stations. Firstly, the K-means clustering method is used to cluster the data, and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division. On this basis, the data is fuzzed to form a fuzzy time series. Secondly, a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load, which is used to predict the short-term trend change of load in the distribution stations. Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are [−50, 20] and [−50, 30], while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are [−20, 15] and [−20, 25]. It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations. [ABSTRACT FROM AUTHOR]

Electricity is the most important and useful energy form for our modern society. This calls for smart management to have optimal usage. Artificial neural networks (ANNs) have emerged as a powerful tool in forecasting power demand, especially when multiple variables are involved. In this paper, taking a city in Morocco as an illustrating example, a long short-term memory (LSTM) neural network is developed to predict power consumption via five weather factors. Our numerical results corroborate the merits of LSTM in forecasting the load demand with which temperature exhibits the strongest correlation. [ABSTRACT FROM AUTHOR]

South Korea has announced a plan to increase the proportion of renewable energy generation to 20% and reduce traditional energy generation by 2030. Among renewable energy resources, wind power has the advantage of relatively low power generation costs. However, it is difficult to forecast, as the output varies significantly depending on changing wind conditions such as the temperature, wind speed, and wind direction. We believe that short-term wind energy forecasts are the most important part for coping with these fluctuations and minimizing scheduling errors, thereby making the grid more reliable and reducing market service costs. Accordingly, we proposed a practical short-term wind power output forecasting method using a novel ensemble model based on a wavelet transform and autoregressive integrated moving average with explanatory variable (ARIMAX) approach. To demonstrate that the model has a good forecasting performance, we applied historical wind speed and wind power output data obtained from Jeju Island's wind farm to the model, and compared them with forecasted values. The normalized mean absolute error (NMAE) was used as the error metric. The comparison results were described for three supervisory control and data acquisition points. The average NMAE was approximately 3%. In addition, an N-1 contingency analysis was conducted to check the voltage profiles and flow limits in the context of a real power system, to ensure that the power system operated stably even with the forecasted values. The system worked successfully with the forecasted values, and can be deployed as application software for energy management systems in South Korea. • The Ensemble model based on Wavelet transform and ARIMAX modelling is applied for a short-term wind power forecasting. • The practical scenarios based on a short-term wind power forecasting is applied to security analysis of power grids. • The proposed methodology is applied the empirical data of JEJU Island's wind farms and power grids. [ABSTRACT FROM AUTHOR]

The change in the electricity demand pattern globally due to sudden extreme weather conditions or situations like COVID 19 pandemic has brought unanticipated challenges for the electric utilities and operators around the world. This work primarily deals with the issue of load forecasting during such type of high impact low frequency (HILF) events. In this paper, we propose a novel resilient short-term load forecasting model capable of producing good forecasting performance for normal as well as critical situations during the COVID 19 pandemic and will also be useful for load forecasting for other HILF situations like natural calamity effect on load demand of the power system. The proposed method uses a feed-forward neural network (FFNN) with an added training feature named resiliency factor to forecast load in both regular and special scenarios. The resiliency factor for any type of node in the distribution system is decided by the power utility using the historical data and declared in advance. The proposed model is tested using the smart metered data available from a real-life distribution grid of an academic cum residential campus. The model is giving satisfactory results for both normal as well as COVID scenario for the said network. [ABSTRACT FROM AUTHOR]

Aiming at the nonlinear and non‐stationary characteristics of the power load, in order to improve the accuracy of load forecasting, a short‐term load forecasting method based on variational modal decomposition and the combination of convolutional neural network and bi‐directional long short‐term memory (Bi‐LSTM) network is proposed. The load sequence is decomposed into components with different frequencies by variational modal decomposition, and each component is predicted by Bi‐LSTM. Then the convolution neural network is introduced to process multi‐source data. The load, temperature, day and other data are constructed according to the time sliding window as the input, and the convolution neural network is used to extract the effective feature vector, the eigenvector is constructed in a time series and used as the input data of Bi‐LSTM network. Taking the public data set provided by a US public utility department as an example, the proposed model is compared with other methods. The results show that the proposed method can better track the change of load and effectively improve the accuracy of short‐term load forecasting. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Based on the analysis of the test data and Miner's rule, a combined high and low cycle fatigue (CCF) life prediction model considering the loading interaction is established by introducing coupled damage. The coupled damage is determined by multiplying the high cycle fatigue damage and the low cycle fatigue damage by a coupled damage coefficient K. According to the analysis of the test data, the coupled damage coefficient K is positively correlated with the high cycle stress ratio R. In addition, an interaction factor, which reflects the loading interaction effect, is introduced to correct the coefficient K. The CCF experimental data of four materials are employed for model verification and comparison among the proposed model, Miner's rule, and Trufyakov–Kovalchuk model (T–K model). The proposed model exhibits a relatively higher prediction accuracy and is more suitable for predicting the fatigue life of CCF. Highlights: A combined fatigue life prediction model is proposed considering loading interaction.Coupled damage coefficient is determined through analysis of the test data.The proposed model exhibits a relatively higher prediction accuracy. [ABSTRACT FROM AUTHOR]

Since the existing prediction methods have encountered difficulties in processing themultiple influencing factors in short-term power load forecasting, we propose a bidirectional long short-term memory (BiLSTM) neural network model based on the temporal pattern attention (TPA) mechanism. Firstly, based on the grey relational analysis, datasets similar to forecast day are obtained. Secondly, thebidirectional LSTMlayermodels the data of thehistorical load, temperature, humidity, and date-type and extracts complex relationships between data from the hidden row vectors obtained by the BiLSTM network, so that the influencing factors (with different characteristics) can select relevant information from different time steps to reduce the prediction error of the model. Simultaneously, the complex and nonlinear dependencies between time steps and sequences are extracted by the TPA mechanism, so the attention weight vector is constructed for the hidden layer output of BiLSTM and the relevant variables at different time steps are weighted to influence the input. Finally, the chaotic sparrow search algorithm (CSSA) is used to optimize the hyperparameter selection of the model. The short-term power load forecasting on different data sets shows that the average absolute errors of short-termpower load forecasting based on our method are 0.876 and 4.238, respectively, which is lower than other forecastingmethods, demonstrating the accuracy and stability of our model. [ABSTRACT FROM AUTHOR]

The ever-growing modern smart grid with more distributed energy resources is providing efficient energy supply while facing several challenges that include harmonics induced among many. Previous and present literature shows that various machine and deep learning models are superior and accurate as compared to the traditional and conventional signal processing techniques. Obtaining accurate results becomes extremely important especially the fact that harmonics are essentially nonlinear, nonparametric, and adaptive in nature. This paper proposes a novel forecasting model that aggregates two deep learning models: convolutional neural network (CNN) and long short term memory (LSTM) recurrent neural network (RNN) detect and forecast harmonics in a power system. CNN-LSTM hybrid forecasting model for harmonics in the power grid system has achieved significantly superior performance in collaborative data mining on spatiotemporal measurement data. Sample features are extracted using CNN before they are passed through LSTM for prediction. To show the superiority of the hybrid CNN-LSTM deep neural prediction network model, it is compared with CNN, LSTM and NARX (Non-Linear Autoregressive with External (Exogenous) Input). CNN-LSTM forecasting performance is superior as compared to the other four models. MSE and RMSE for CNN-LSTM are 0.00038 (〖3.8 × 10〗^ (−4)) and 0.0000014917 (1.4917 × 10^(−6)) respectively. [ABSTRACT FROM AUTHOR]

With the continuous economic growth, the number of power customers has increased significantly, and consumers in the field of power marketing will inevitably have a credit crisis. In order to reduce the business risk of relevant departments and improve the risk prediction ability of the system, this paper evaluates and reviews the user credit system. In this paper, the basic structure of BP neural network is described firstly, and then, the traditional BP neural network model is optimized after analyzing its algorithm flow. Based on this point, this paper analyzes the characteristics of customers in the energy and electricity market in the research area, and referring to local experts who have been engaged in power sales for many years, this paper puts forward a new set of directly scored power system load forecasting index system and algorithm improvement scheme and discusses the evaluation of power market credit rating based on the credit evaluation suggestions of power customers. After establishing the judgment criteria, in this paper, the power load data of the target area are studied by empirical analysis method and select three different customers from the production area, commercial and residential areas and residential areas as cases to analyze the determination of their credit rating and then, discuss the results of regional power load forecasting. Finally, this paper puts forward a kind of power management method based on the user's credit rating, and in order to complete the modernization transformation of power management system and promote the market development. In this paper, after improving and optimizing the traditional BP neural network, it is applied to the power market to predict the target user credit system, so as to achieve the improvement of forecasting ability. [ABSTRACT FROM AUTHOR]

With photovoltaic power being increasingly integrated into power grid, accurately forecasting solar irradiance is of critical for ensuring stable and economical operation of power systems. The forecast accuracy in ultra-short-term horizons can be greatly improved by employing ground-based sky images. Although wide range of computer vision methods have been used for modelling, effectively extracting spatiotemporal features from sky image sequence is still a tough task. In this study, a sparse spatiotemporal feature descriptor is introduced to enhance the process of dynamic spatiotemporal information extraction from continuous grayscale sky images, while spatial pyramid pooling is used for feature refinement. Parallelly, dense convolutional network is used to extract static features from the nearest single-frame RGB sky images. Both dynamic and static spatiotemporal features were adequately extracted and subsequently fused for the multi-step prediction of global horizontal irradiance. In addition, various competitive models in object detection are adopted as benchmarks for comparison. The experimental results revealed that the proposed method outperformed baseline models, with up to 5.51% reduction on normalized root mean square error (NRMSE) and 9.38% improvement on ramp event forecast. The proposed method can be widely applied to photovoltaic stations equipped with all-sky-imagers. [ABSTRACT FROM AUTHOR]

Wind power prediction is an important research topic in the wind power industry and many prediction algorithms have recently been studied for the sake of achieving the goal of improving the accuracy of short-term forecasting in an effective way. To tackle the issue of generating a huge transition matrix in the traditional Markov model, this paper introduces a real-time forecasting method that reduces the required calculation time and memory space without compromising the prediction accuracy of the original model. This method is capable of obtaining the state probability interval distribution for the next moment through real-time calculation while preserving the accuracy of the original model. Furthermore, the proposed Markov-based Back Propagation (BP) neural network was optimized using the Particle Swarm Optimization (PSO) algorithm in order to effectively improve the prediction approach with an improved PSO-BP neural network. Compared with traditional methods, the computing time of our improved algorithm increases linearly, instead of growing exponentially. Additionally, the optimized Markov-based PSO-BP neural network produced a better predictive effect. We observed that the Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE) of the prediction model were 12.7% and 179.26, respectively; compared with the existing methods, this model generates more accurate prediction results. [ABSTRACT FROM AUTHOR]

Since traffic congestion during peak hours has become the norm in daily life, research on short-term traffic flow forecasting has attracted widespread attention that can alleviate urban traffic congestion. However, the existing research ignores the uncertainty of short-term traffic flow forecasting, which will affect the accuracy and robustness of traffic flow forecasting models. Therefore, this paper proposes a short-term traffic flow forecasting algorithm combining the cloud model and the fuzzy inference system in an uncertain environment, which uses the idea of the cloud model to process the traffic flow data and describe its randomness and fuzziness at the same time. First, the fuzzy c-means algorithm is selected to carry out cluster analysis on the original traffic flow data, and the number and parameter values of the initial membership function of the system are obtained. Based on the cloud reasoning algorithm and the cloud rule generator, an improved fuzzy reasoning system is proposed for short-term traffic flow predictions. The reasoning system cannot only capture the uncertainty of traffic flow data, but it also can describe temporal dependencies well. Finally, experimental results indicate that the proposed model has a better prediction accuracy and better stability, which reduces 0.6106 in RMSE, reduces 0.281 in MAE, and reduces 0.0022 in MRE compared with the suboptimal comparative methods. [ABSTRACT FROM AUTHOR]

To enhance the control effect of intelligent transportation system (ITS) of expressway, avoid blind travel causing traffic congestion, improve road capacity, and achieve the purpose of smooth and efficient operation of the network, 3σ criterion and 2σ cycle removal criterion were used to eliminate the abnormal data of travel time. Thereafter, a combined prediction model of Long Short‐Term Memory (LSTM) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and attention mechanism is proposed. Travel time data, collected from the real world, is decomposed into several Intrinsic Mode Functions (IMFs) and a residue with the CEEMDAN. Then, the decomposed sub‐sequence is predicted and the final prediction result is obtained by comprehensive superposition. The results indicate that: the CEEMDAN algorithm has significantly improved the prediction performance. Compared with the AT‐LSTM model, mean absolute percentage error (MAPE) is reduced by 10.75%, 24.84%, 31.80%, 24.98%, 14.29%, and 31.42% when the time window was 3, 6, 9, 12, 15, and 18, respectively, in the dataset of Yaxi expressway. In the dataset of Chengnan expressway, the number of error points with MAPE greater than 40% is significantly reduced after the adoption of the LSTM model. After the introduction of CEEMDAN algorithm, the error at each time point is controlled within 30%. The proposed model controls the error at each time point within 25%. In different datasets, the proposed model has better prediction ability for highly non‐linear travel time, and can better capture the trend abrupt characteristics of rush hour traffic flow. [ABSTRACT FROM AUTHOR]