Search

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]

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]

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]

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]

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]

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]

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]

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]

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]

There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%. [ABSTRACT FROM AUTHOR]

In recent years, electrical systems have evolved, creating uncertainties in short-term economic dispatch programming due to demand fluctuations from self-generating companies. This paper proposes a flexible Machine Learning (ML) approach to address electrical load forecasting at various levels of disaggregation in the Peruvian Interconnected Electrical System (SEIN). The novelty of this approach includes utilizing meteorological data for training, employing an adaptable methodology with easily modifiable internal parameters, achieving low computational cost, and demonstrating high performance in terms of MAPE. The methodology combines modified Fuzzy ARTMAP Neural Network (FAMM) and hybrid Support Vector Machine FAMM (SVMFAMM) methods in a parallel process, using data decomposition through the Wavelet filter db20. Experimental results show that the proposed approach outperforms state-of-the-art models in predicting accuracy across different time intervals. [ABSTRACT FROM AUTHOR]

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems' reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class's main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach's advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions. [ABSTRACT FROM AUTHOR]

The short-term forecast of photovoltaic (PV) power is crucial for the security and economics of power system operations. However, the fluctuation characteristics of the PV power, which are closely related to the meteorological factors, introduce inaccuracies in its forecast. Towards this end, the paper studies the effects of clustering analysis at long time scale and data reconstruction technique at short time scale on capturing PV power fluctuation characteristics. A short-term PV power forecasts method based on multi-scale fluctuation characteristics extraction (MFCE), which employs a path analysis to identify the relevance of meteorological factors with PV power at long time scale and a phase space reconstruction to analyze PV power fluctuation characteristics at short time scale, is proposed in this paper. The proposed MFCE methodology deploys a widely-used extreme gradient boosting (XGBoost) model to output the forecasting results. Both the effectiveness and accuracy of the proposed methodology are verified by using the real data under the conditions of sunny and cloudy days of four seasons compared to traditional methodologies. • The multi-scale fluctuation characteristics are analyzed for PV power forecast. • Identify dominant meteorological factors affecting PV power by path analysis. • The hour-level fluctuation is reduced by using path analysis-based clustering. • The phase space reconstruction is used to reduce the minute-level fluctuation. [ABSTRACT FROM AUTHOR]

Runoff prediction with high accuracy is vital to protect people's properties and lives. In this study, a Short-Term Streamflow Forecasting Framework combining Long Short-Term Memory (LSTM) with Self-Organizing Map (SOM) is proposed, which clusters hydro-meteorological factors through SOM network to cluster the streamflow into several flow modes with different physical characteristics, converting complex global modeling into local modeling. The framework also considers the temporal characteristics of the streamflow, so the data of different flow modes are fitted with multiple LSTM separately. Besides, Random Forest is used in the framework for feature selection. The model is used for streamflow forecasting 1 to 3 day(s) ahead in the Qingxi River Basin in China, and the performance is compared with those of SVR, ANN, LSTM, SOM-SVR, SOM-ANN, FCM-LSTM, and K-means-LSTM. In addition, SOM-LSTM with three and four clusters were compared. The results show that (1) the sub-processes obtained by SOM clustering show some physical characteristics consistent with the actual process. (2) SOM-LSTM significantly improves the prediction accuracy of short-term streamflow forecasting. (3) All the integrated models using different clustering methods can improve the prediction accuracy of a single LSTM, and SOM-LSTM has the most reliable prediction when the lead time is 1–2 day(s). (4) The number of clusters being set to 4 is more suitable for the proposed prediction framework. The overall results show that the SOM-LSTM provides an efficient method for short-term streamflow forecasting. [ABSTRACT FROM AUTHOR]

Accurate prediction of wind power is of great significance to the stable operation of the power system and the vigorous development of the wind power industry. In order to further improve the accuracy of ultra-short-term wind power forecasting, an ultra-short-term wind power forecasting method based on the CGAN-CNN-LSTM algorithm is proposed. Firstly, the conditional generative adversarial network (CGAN) is used to fill in the missing segments of the data set. Then, the convolutional neural network (CNN) is used to extract the eigenvalues of the data, combined with the long short-term memory network (LSTM) to jointly construct a feature extraction module, and add an attention mechanism after the LSTM to assign weights to features, accelerate model convergence, and construct an ultra-short-term wind power forecasting model combined with the CGAN-CNN-LSTM. Finally, the position and function of each sensor in the Sole du Moulin Vieux wind farm in France is introduced. Then, using the sensor observation data of the wind farm as a test set, the CGAN-CNN-LSTM model was compared with the CNN-LSTM, LSTM, and SVM to verify the feasibility. At the same time, in order to prove the universality of this model and the ability of the CGAN, the model of the CNN-LSTM combined with the linear interpolation method is used for a controlled experiment with a data set of a wind farm in China. The final test results prove that the CGAN-CNN-LSTM model is not only more accurate in prediction results, but also applicable to a wide range of regions and has good value for the development of wind power. [ABSTRACT FROM AUTHOR]

Accurate and efficient short-term forecasting of multiple loads is of great significance to the operation control and scheduling of integrated energy distribution systems. In order to improve the effect of load forecasting, a mogrifier-quantum weighted memory enhancement long short-term memory (Mogrifier-QWMELSTM) neural network forecasting model is proposed. Compared with the conventional LSTM neural network model, the model proposed in this paper has three improvements in model structure and model composition. First, the mogrifier is added to make the data fully interact with each other. This addition can help enhance the correlation between the front and rear data and improve generalization, which is the main disadvantage of LSTM neural network. Second, the memory enhancement mechanism is added on the forget gate to realize the extraction and recovery of forgotten information. The addition can help improve the gradient transmission ability in the learning process of the neural network, make the neural network remain sensitive to distant data information, and enhance the memory ability. Third, the model is composed of quantum weighted neurons. Compared with conventional neurons, quantum weighted neurons have significant advantages in nonlinear data processing and parallel computing, which help to improve the accuracy of load forecasting. The simulation results show that the weighted mean accuracy of the proposed model can reach more than 97.5% in summer and winter. Moreover, the proposed model has good forecasting effect on seven typical days in winter, which shows that the model has good stability. [ABSTRACT FROM AUTHOR]

The need for and interest in very-short-term load forecasting (VSTLF) is increasing and important for goals such as energy pricing markets. There is greater challenge in predicting load consumption for residential-load-type data, which is highly variable in nature and does not form visible patterns present in aggregated nodal-type load data. Previous works have used methods such as LSTM and CNN for VSTLF; however, the use of DNN has yet to be investigated. Furthermore, DNNs have been effectively used in STLF but have not been applied to very-short-term time frames. In this work, a deep network architecture is proposed and applied to very-short-term forecasting of residential load patterns that exhibit high variability and abrupt changes. The method extends previous work by including delayed load demand as an input, as well as working for 1 min data resolution. The deep model is trained on the load demand data of selected days—one, two, and a week—prior to the targeted day. Test results on real-world residential load patterns encompassing a set of 32 days (a sample from different seasons and special days) exhibit the efficiency of the deep network in providing high-accuracy residential forecasts, as measured with three different error metrics, namely MSE, RMSE, and MAPE. On average, MSE and RMSE are lower than 0.51 kW and 0.69 kW, and MAPE lower than 0.51%. [ABSTRACT FROM AUTHOR]

Large-scale integration of photovoltaics (PV) into electricity grids is challenged by the intermittent nature of solar power. Sky-image-based solar forecasting using deep learning has been recognized as a promising approach to predicting the short-term fluctuations. However, there are few publicly available standardized benchmark datasets for image-based solar forecasting, which limits the comparison of different forecasting models and the exploration of forecasting methods. To fill these gaps, we introduce SKIPP'D—a SKy Images and Photovoltaic Power Generation Dataset. The dataset contains three years (2017–2019) of quality-controlled down-sampled sky images and PV power generation data that is ready-to-use for short-term solar forecasting using deep learning. In addition, to support the flexibility in research, we provide the high resolution, high frequency sky images and PV power generation data as well as the concurrent sky video footage. We also include a code base containing data processing scripts and baseline model implementations for researchers to reproduce our previous work and accelerate their research in solar forecasting. • A curated sky image and PV generation dataset is released for short-term solar forecasting. • Processed benchmark data and raw data are both provided for flexibility of research. • Reference codes for data processing and baseline model implementations are provided. • Baseline deep learning models are developed to demonstrate the uses of the dataset. [ABSTRACT FROM AUTHOR]

The construction of smart grids has greatly changed the power grid pattern and power supply structure. For the power system, reasonable power planning and demand response is necessary to ensure the stable operation of a society. Accurate load prediction is the basis for realizing demand response for the power system. This paper proposes a Pre-Attention-CNN-GRU model (PreAttCG) which combines a convolutional neural network (CNN) and gate recurrent unit (GRU) and applies the attention mechanism in front of the whole model. The PreAttCG model accepts historical load data and more than nine other factors (including temperature, wind speed, humidity, etc.) as input. The attention layer and CNN layer effectively extract the features and weights of each factor. Load forecasting is then performed by the prediction layer, which consists of a stacked GRU. The model is verified by industrial load data from a German dataset and a Chinese dataset from the real world. The results show that the PreAttCG model has better performance (3~5% improvement in MAPE) than both LSTM with only load input and LSTM with all factors. Additionally, the experiments also show that the attention mechanism can effectively extract the weights of relevant factors affecting the load data. [ABSTRACT FROM AUTHOR]

With the deepening reform of the power system, power sales companies need to adopt new power sales strategies to provide customers with better economic marketing solutions. Customer-side configuration of an energy storage system (ESS) can participate in power-related policies to reduce the comprehensive cost of electricity for commercial and industrial customers and improve customer revenue. For power sales companies, this can also attract new customers, expand sales and quickly capture the market. However, most of the ESS evaluation models studied so far are based on historical data configuration of typical daily storage capacity and charging and discharging scheduling instructions. In addition, most models do not adequately consider the performance characteristics of the ESS and cannot accurately assess the economics of the energy storage model. This study proposes an intelligent power sales strategy based on load forecasting with the participation of optimal allocation of ESS. Based on long short-term memory (LSTM) artificial neural network for predictive analysis of customer load, we evaluate the economics of adding energy storage to customers. Based on the premise of the two-part tariff, the ESS evaluation model is constructed with the objective of minimizing the annual comprehensive cost to the user by considering the energy tariff and the savings benefits of the basic tariff, assessing the annualized cost of ESS over its entire life cycle, and the impact of battery capacity decay on economics. The particle swarm optimization (PSO) algorithm is introduced to solve the model. By simulating the arithmetic example for real customers, their integrated electricity costs are significantly reduced. Moreover, this smart power sales strategy can provide different sales strategies according to the expected payback period of customers. This smart sales strategy can output more accurate declared maximum demand values than other traditional sales strategies, providing a more economical solution for customers. [ABSTRACT FROM AUTHOR]

To address the problems of "difficult to consume" renewable energy and the randomness of power output, we propose the CHP unit joint-operation model with power to gas (P2G) and carbon capture system (CCS) technologies and analyze the operation cost, carbon emission, and "electric-heat coupling" characteristics of this model. A dispatch optimization model is constructed based on the information-gap decision theory under the strategy to further consider the interval uncertainty of renewable energy unit output and load forecast. The optimized-dispatching model effectively solves the fate of renewable unit output and electric-thermal load and provides dispatching strategies for decision-makers to balance risk and capital management. [ABSTRACT FROM AUTHOR]

Recently, neural networks based on intelligent algorithms have been widely used in short-term power load prediction. However, these algorithms have poor reproducibility in the case of repetition. Aiming at the shortcomings of the gray wolf optimizer (GWO) algorithm, such as slow convergence speed and easy to fall into local optimum, an improved gray wolf optimizer (IGWO) was proposed. In order to better extract features from time series data, the improved grey wolf optimizer-bidirectional long short-term memory (IGWO-BILSTM) power load prediction model is established by bidirectional long short-term memory (BILSTM). Pearson correlation analysis uses the actual power load data set of a certain region and selects the highly correlated factors as the input of the network. The BILSTM model was used to train the preprocessed data, the IGWO algorithm was used to find the optimal solution parameters during the training, and finally, the optimal prediction results were output to the test set. The experimental results show that compared with the prediction model established by the traditional optimization algorithm, the mean absolute percentage error (MAPE) value of the IGWO-BILSTM model is 1.63, the root mean square error (RMSE) value is 1.47, and the mean absolute error (MAE) value is 1.17. The convergence speed is faster and the prediction result is more accurate. The accuracy of multidimension load forecasting is greatly improved. [ABSTRACT FROM AUTHOR]

Based on the counted power system emission factors of North China Power Grid, a community carbon emissions sample database is constructed. The support vector regression (SVR) model is trained to forecast the power carbon emissions, which is optimized by genetic algorithm (GA). A community carbon emission warning system is designed according the results. The dynamic emission coefficient curve of the power system is obtained by fitting the annual carbon emission coefficients. The time series SVR carbon emission prediction model is constructed, while the GA is improved to optimize its parameters. Taking Beijing Caochang Community as an example, a carbon emission sample database is generated based on the electricity consumption and emission coefficient curve to train and test the SVR model. The results show that the GA–SVR model fits well with the training set and the testing set, and the prediction accuracy of the testing set reaches 86%. In view of the training model in this paper, the carbon emission trend of community electricity consumption in the next month is predicted. The carbon emission warning system of the community is designed, and the specific strategy of community carbon emission reduction is proposed. [ABSTRACT FROM AUTHOR]

The dynamic load in the soil directly leads to the damage of underground structures upon explosions. In this study, a method to predict blast load on underground structure surface based on the neural network was developed to study the load distribution under close-in detonation. First, taking the underground utility tunnel as the experimental structure, 52 groups of field blast tests were conducted on the surface load mechanism, and the surface load data samples were obtained. Second, the key influencing parameters of the reflected blast load were obtained through the dimensional analysis method, and the backpropagation neural network model was constructed based on the test data using the Levenberg–Marquardt algorithm to train and optimize the neural network. Finally, the accuracy of load prediction results was compared and evaluated among the neural network, empirical formula, and nonlinear regression analysis (NRA) methods. It is found that the input parameters of combined variables can further improve the prediction accuracy of the neural network compared with the input parameters of single physical variables. Compared with the empirical formula method and the NRA method, the neural network model with input parameters of combined variables provided the most accurate prediction. The load distribution under typical conditions calculated by the neural network showed that the explosive setting parameters impact the uneven shape of blast load on the structure surface. The increase in explosive equivalent and depth reduces the nonuniformity of load distribution, while the decrease in explosion distance increases the nonuniformity of load distribution. [ABSTRACT FROM AUTHOR]

Wind speed forecasting is advantageous in reducing wind-induced accidents or disasters and increasing the capture of wind power. Accordingly, this forecasting process has been a focus of research in the field of engineering. However, because wind speed is chaotic and random in nature, its forecasting inevitably includes errors. Consequently, specifying the appropriate method to obtain accurate forecasting results is difficult. The probabilistic forecasting method has considerable relevance to short-term wind speed forecasting because it provides both the predicted value and the error distribution. This study proposes a probabilistic forecasting method for short-term wind speeds based on the Gaussian mixture model and long short-term memory. The precision of the proposed method is evaluated by prediction intervals (i.e., prediction interval coverage probability, prediction interval normalized average width, and coverage width-based criterion) using 29 monitored wind speed datasets. The effects of wind speed characteristics on the forecasting precision of the proposed method were further studied. Results show that the proposed method is effective in obtaining the probability distribution of predicted wind speeds, and the forecast results are highly accurate. The forecasting precision of the proposed method is mainly influenced by the wind speed difference and standard deviation. [ABSTRACT FROM AUTHOR]

To improve the accuracy of short-term wind speed forecasting, we proposed a Gated Recurrent Unit network forecasting method, based on ensemble empirical mode decomposition and a Grid Search Cross Validation parameter optimization algorithm. In this study, first, in the process of decomposing, the set empirical mode of decomposition was introduced to divide the wind time series into high-frequency modal, low-frequency modal, and trend modal, using the Pearson correlation coefficient. Second, during parameter optimization, the grid parameter optimization algorithm was employed in the GRU model to search for the combination of optimal parameters. Third, the improved GRU model was driven with the decomposed components to predict the new components, which were used to obtain the predicted wind speed by modal reorganization. Compared with other models (i.e., the LSTM, GS-LSTM, EEMD-LSTM, and the EEMD-GS-LSTM), the proposed model was applied to the case study on wind speed of a wind farm, located in northwest China. The results showed that the presented forecasting model could reduce the forecasting error (RMSE) from 1.411 m/s to 0.685 m/s and can improve the accuracy of forecasts. This model provides a new approach for short-term wind speed forecasting. [ABSTRACT FROM AUTHOR]

Increased demand for fast edge computation and privacy concerns have shifted researchers' focus towards a type of distributed learning known as federated learning (FL). Recently, much research has been carried out on FL; however, a major challenge is the need to tackle the high diversity in different clients. Our research shows that using highly diverse data sets in FL can lead to low accuracy of some local models, which can be categorised as anomalous behaviour. In this paper, we present FedBranched, a clustering-based framework that uses probabilistic methods to create branches of clients and assigns their respective global models. Branching is performed using hidden Markov model clustering (HMM), and a round of branching depends on the diversity of the data. Clustering is performed on Euclidean distances of mean absolute percentage errors (MAPE) obtained from each client at the end of pre-defined communication rounds. The proposed framework was implemented on substation-level energy data with nine clients for short-term load forecasting using an artificial neural network (ANN). FedBranched took two clustering rounds and resulted in two different branches having individual global models. The results show a substantial increase in the average MAPE of all clients; the biggest improvement of 11.36% was observed in one client. [ABSTRACT FROM AUTHOR]

Accurate multivariate load forecasting plays an important role in the planning management and safe operation of integrated energy systems. In order to simultaneously reduce the prediction bias and variance, a hybrid ensemble learning method for load forecasting of an integrated energy system combining sequential ensemble learning and parallel ensemble learning is proposed. Firstly, the load correlation and the maximum information coefficient (MIC) are used for feature selection. Then the base learner uses the Boost algorithm of sequential ensemble learning and uses the Bagging algorithm of parallel ensemble learning for hybrid ensemble learning prediction. The grid search algorithm (GS) performs hyper-parameter optimization of hybrid ensemble learning. The comparative analysis of the example verification shows that compared with different types of single ensemble learning, hybrid ensemble learning can better balance the bias and variance and accurately predict multiple loads such as electricity, cold, and heat in the integrated energy system. [ABSTRACT FROM AUTHOR]

Worldwide, the demand for power load forecasting is increasing. A multi-step power-load forecasting model is established based on Informer, which takes the historical load data as the input to realize the prediction of the power load in the future. The constructed model abandons the common recurrent neural network to deal with time-series problems, and uses the seq2seq structure with sparse self-attention mechanism as the main body, supplemented by specific input and output modules to deal with the long-range relationship in the time series, and makes effective use of the parallel advantages of the self-attention mechanism, so as to improve the prediction accuracy and prediction efficiency. The model is trained, verified and tested by using the power-load dataset of the Taoyuan substation in Nanchang. Compared with RNN, LSTM and LSTM with the attention mechanism and other common models based on a cyclic neural network, the results show that the prediction accuracy and efficiency of the Informer-based power-load forecasting model in 1440 time steps have certain advantages over cyclic neural network models. [ABSTRACT FROM AUTHOR]

Accurate medium- and long-term power load forecasting is of great significance for the scientific planning and safe operation of power systems. Monthly power load has multiscale time series correlation and seasonality. The existing models face the problems of insufficient feature extraction and a large volume of prediction models constructed according to seasons. Therefore, a hybrid feature pyramid CNN-LSTM model with seasonal inflection month correction for medium- and long-term power load forecasting is proposed. The model is constructed based on linear and nonlinear combination forecasting. With the aim to address the insufficient extraction of multiscale temporal correlation in load, a time series feature pyramid structure based on causal dilated convolution is proposed, and the accuracy of the model is improved by feature extraction and fusion of different scales. For the problem that the model volume of seasonal prediction is too large, a seasonal inflection monthly load correction strategy is proposed to construct a unified model to predict and correct the monthly load of the seasonal change inflection point, so as to improve the model's ability to deal with seasonality. The model proposed in this paper is verified on the actual power data in Shaoxing City. [ABSTRACT FROM AUTHOR]

Main Forecasting Problems There are different types of forecasting problems that can arise in electricity demand forecasting, including: Very short-term forecasting, which refers to the prediction of electricity demand or production for a time horizon from seconds to a few hours ahead. The applications of long-term forecasts of wind farm power generation are, e.g., maintenance scheduling, wind farm design, electricity market restructuring, and optimization of operating costs. A modern power system is a complex network of interconnected components, such as generators, transmission lines, and distribution subsystems, that are designed to provide electricity to consumers in an efficient and reliable manner. A power system requires forecasts that predict the future electricity demand, the power generation from RESs, and meteorological data that are important regarding consumer demand and the level of generation from RESs. [Extracted from the article]

Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation. [ABSTRACT FROM AUTHOR]

Short-term road maintenance activity is an important factor leading to nonrecurring congestion. To reduce such congestion, a nonparametric method is proposed to optimize the maintenance operation start time. A realtime traffic map is adopted as the traffic data source. This approach is of low cost, provides timely data updates, and includes wide road network coverage. Corresponding data acquisition and data conversion methods are proposed, and traffic data (e.g., speed and volume) are acquired under maintenance and normal conditions. A public travel open platform is used to obtain maintenance activity data. Based on these data, a nonparametric model, a support vector machine for regression (SVR), is established to estimate the traffic delay caused by maintenance operations. The optimal start time that leads to the minimum total cost can be obtained by assessing all possible operation start times and calculating the associated costs, such as maintenance, accident, and delay costs. The method is applied to the Shanghai expressway network to optimize the start time of maintenance operations. A total of 159 short-term maintenance activities and more than 300,000 corresponding real-time traffic records are considered, and continuously updating is performed. The SVR model is established and used to predict the traffic delay of actual maintenance activities with different operation start times. The results show that the proposed method is effective for start time optimization in complex scenarios. Compared with the traditional parametric method, the proposed method yields an increase in the delay estimation accuracy based on ground truth data. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of in-depth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model. [ABSTRACT FROM AUTHOR]

Accurate short-term load forecasting is of great significance to the safe and stable operation of power systems and the development of the power market. Most existing studies apply deep learning models to make predictions considering only one feature or temporal relationship in load time series. Therefore, to obtain an accurate and reliable prediction result, a hybrid prediction model combining a dual-stage attention mechanism (DA), crisscross grey wolf optimizer (CS-GWO) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. DA is introduced on the input side of the model to improve the sensitivity of the model to key features and information at key time points simultaneously. CS-GWO is formed by combining the horizontal and vertical crossover operators, to enhance the global search ability and the diversity of the population of GWO. Meanwhile, BiGRU is optimized by CS-GWO to accelerate the convergence of the model. Finally, a collected load dataset, four evaluation metrics and parametric and non-parametric testing manners are used to evaluate the proposed CS-GWO-DA-BiGRU short-term load prediction model. The experimental results show that the RMSE, MAE and SMAPE are reduced respectively by 3.86%, 1.37% and 0.30% of those of the second-best performing CSO-DA-BiGRU model, which demonstrates that the proposed model can better fit the load data and achieve better prediction results. [ABSTRACT FROM AUTHOR]

Accurate PV power forecasting is becoming a mandatory task to integrate the PV plant into the electrical grid, scheduling and guaranteeing the safety of the power grid. In this paper, a novel model to forecast the PV power using LSTM-TCN has been proposed. It consists of a combination between Long Short Term Memory and Temporal Convolutional Network models. LSTM is used to extract the temporal features from input data, then combined with TCN to build the connection between features and outputs. The proposed model has been tested using a dataset that includes historical time series of measured PV power. The accuracy of this model is then compared to LSTM and TCN models in different seasons, time periods forecast, cloudy, clear, and intermittent days. For one step forecasting, the results show that our proposed model outperforms the LSTM and TCN model. It has carried out a reduction of 8.47%, 14.26% for the autumn season, 6.91%,15.18 for the winter season, 10.22%,14.26% for spring season and 14.26%, 14.23% for the summer season on the Mean Absolute Error compared with LSTM, TCN. For multistep forecasting, LSTM-TCN surpassed all compared models in different time periods forecast from 2 steps to 7 steps PV power forecasting. [ABSTRACT FROM AUTHOR]

Offshore wind turbines are exposed during their serviceable lifetime to a wide range of loads from aero-, hydro- and structural dynamics. This complex loading scenario will have an impact on the lifetime of the asset, with fatigue remaining the key structural design driver for the substructure, e.g. the monopile. The ability to monitor the progression of fatigue life of these assets has recently become an operational concern. To achieve a monitoring alternative to strain gauges (cost-prohibitive farm-wide installation), supervisory control and data acquisition (SCADA) systems, often coupled with acceleration measurements, have been used. Existing work focused primarily on ten-minute fatigue load estimation. However, fatigue accumulates over time and the ability to accurately monitor this accumulation of fatigue over a longer time-window is paramount. In this contribution we investigate a novel approach using nine months of real-world SCADA and acceleration ten-minute statistics as inputs of a neural network model for long-term DEM estimation. This is further enhanced by including physical information relative to the problem at hand into the neural network model, in a so-called physics-informed machine learning approach. Specifically, we employ a custom loss function – the Minkowski logarithmic error – which prioritizes conservativeness (over-prediction of fatigue rates) and to embed the damage accumulation into the machine learning model. In the results and discussion section, we use Tower-TP interface load measurements for three real-world turbines to demonstrate the concept. First a model is trained on one turbine, before being applied to all three locations. The model performance is compared to direct measurements of fatigue progression at all three locations and a control loss function (the mean squared logarithm error) in both the Fore-Aft and Side-Side loading directions. The novel physics-informed model clearly outperforms the control loss function in accumulated fatigue predictions with errors below 3% on accumulated damage equivalent moment estimates. Additionally, the model's long-term performance is checked according to varying timescales (hourly, daily, weekly or monthly accumulation of DEM) and it is seen that the spread on the error rapidly reduces and converges to zero. Finally, the long-term DEM is accumulated into a monthly accumulated fatigue damage. We also see how the error on this begins to converge after 6+ months, which opens the door for extrapolation. • RUL/fatigue estimation on XL monopiles of OWTs using 9 months of real data. • SCADA and acceleration data as alternatives to strain gauge measurements. • Physics-guided learning of neural networks greatly increases model performance. • SCADA and acceleration data can be used for long-term fatigue damage estimation. • Errors on DEM are kept below 3% and on damage below 15%. [ABSTRACT FROM AUTHOR]

Short-term power load forecasting provides important guidance for the improvement of power marketing and control levels of power enterprises. In this paper, a novel method, named RF-TStacking, is proposed to forecast the short-term load. This study starts from the influence factors of the power load, the random forest is applied to estimate the importance of the influence factors of short-term load. Based on Stacking strategy, the integration of LightGBM and random forest is realized to achieve short-term power load forecasting. To improve the generalization ability of the load model, random put back sampling is used to sample each primary learner, and the average value is taken as the result of each primary learner. The Bayesian optimization is used to adjust the super parameters of the model to improve the accuracy of the selection of influencing factors. The load data of a region in northwest China are used for the testing, and it is found that the model can provide stable prediction results. [ABSTRACT FROM AUTHOR]

The energy Internet (EI) equipment may face threats that attackers poison federated learning (FL) models to disturb electricity load forecasting. To mitigate this vulnerability, it is important to study load forecasting disturbance approaches. This article proposes a side-channel analysis (SCA)-based disturbance approach. First, we design an FL SCA scheme to extract power information from the FL chip running forecasting model. Second, we propose an FL data speculation method using an optimized convolutional neural network trained with SCA information. Third, we design a label-flipping-based poisoning scheme with speculated data characteristics for load forecasting disturbance. Experimental results show attackers can successfully poison and disturb FL-based load forecasting. The average accuracy of EI load data speculation is 99.8%. This work is the first to study EI load forecasting disturbance from an SCA perspective. [ABSTRACT FROM AUTHOR]

The manager of the electricity supply chain needs to correctly forecast one of the most important variables affecting the management of the electricity chain. This article has proposed a hybrid model for electricity demand forecasting using deep learning. Firstly, the historical electricity demand data is decomposed using empirical mode decomposition (EMD) algorithm. Then, whale optimization algorithm (WOA) is used to determine signal decomposition levels rounds by EMD and the allocation of signals to neural networks. Parameters that promote accuracy of the forecast are selected using principal component analysis (PCA). A group of signals are fed into a back propagation neural network, whose components are decomposed by wavelet transform. The weights of this neural network are determined by using elephant herding optimization (EHO) algorithm (WTBPNN-EHO). The rest of the signals with higher levels of complexity are fed into the long short-term memory (LSTM) neural network. Finally, the load is calculated by aggregating the results of these two neural networks. Finally, the performance of this model has been compared with other existing models. [ABSTRACT FROM AUTHOR]

Computer servers in cloud data centers are known to consume a huge amount of energy in their operations. For energy saving, load balancing has been used but it is only effective when CPU loads are predicted accurately. Noise in the energy consumption data is often a detrimental factor responsible for the CPU load prediction error. In prior arts, denoising has not been considered as an approach to subside the prediction error. Therefore, a novel prediction approach called CEEMDAN-RIDGE that is centered on denoising is proposed and reported in this paper. Firstly, CEEMDAN is applied to decompose the CPU consumption data which is in the form of a time series. The curvature similarity between a pair of the original series and its corresponding decomposed series is measured. By referencing to this similarity measure, an effective series is obtained from filtration of the noise series. The effective series after the noise series is filtered out is reconstructed to a new fitting curve for CPU load prediction. The prediction accuracy is further enhanced by doing some error correction called RIDGE, which is made possible by predicting the error a priori from the historical error data from the previous prediction. In order to validate CEEMDAN-RIDGE, a series of experiments are conducted with Google trace data. The experiment results show that the LSTM model using the proposed CPU load prediction approach outperforms other models significantly in three performance metrics: RMSE, MAE and MAPE. [ABSTRACT FROM AUTHOR]

Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266. [ABSTRACT FROM AUTHOR]

Photovoltaic power generation fluctuates, photovoltaic output is easily affected by various meteorological characteristics, and traditional TCN networks tend to over-enhance spatial characteristics and weaken individual characteristics. In view of these problem, a short-term photovoltaic power generation prediction model based on VMD and improved TCN is proposed in this study. The original photovoltaic power generation time series is decomposed into several modal components of different frequencies through VMD, and each modal component and the corresponding meteorological data are input to the improved TCN network for modeling and learning. The center frequency method is used to determine the optimal decomposition modal number of VMD. On the basis of the traditional TCN prediction model, DropBlock regularization is used to replace Dropout regularization to achieve the effect of suppressing information synergy in the convolutional layer, and the attention mechanism is introduced to autonomously mine and highlight the impact of key meteorological input characteristics and quantify the impact of various meteorological factors on photovoltaic power generation to improve forecasting precision. Based on the real data of a photovoltaic power station in Jiangsu, the simulation experiments show that the RMSE of the proposed prediction method is 0.62 MW and the MAPE is 2.03%. [ABSTRACT FROM AUTHOR]

As the energy crisis intensifies, wind energy generated by wind turbines, commonly known as a promising renewable energy source, is being more frequently employed. As a result, wind energy forecasting, particularly wind speed forecasting, plays a crucial role for wind energy management. Due to their importance, many wind speed forecasting methods have been proposed. However, many of the traditional prediction models did not account for data pre-processing, or the constraints of an individual forecasting model, resulting in poor prediction accuracy. The purpose of this article is to present a unique forecasting model that incorporates noise-processing methods, statistical techniques, deep learning frameworks, and optimization algorithms to improve on existing methods. The suggested ensemble model was tested using 10-minute wind speed data from real-world conditions. The experimental results show that the mean absolute percentage error of the 10-minute prediction of the proposed model is 5.73%, which is also about 17% improvement compared to the competing model (mean absolute percentage error of 6.71%). [ABSTRACT FROM AUTHOR]