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Forecasting the demand for electrical load precisely is essential for the steady and energy‐efficient operation of today’s power systems, especially in places where there is much variability in demand. In this study, we propose a novel hybrid deep learning framework, termed variational mode decomposition with temporal dual learners network (VMD‐TDLNet), for both short‐term and long‐term electricity load forecasting. At the beginning, VMD is used on the daily peak load time series to extract different intrinsic mode functions (IMFs) of various frequencies. Every decomposed mode is processed in parallel with long short‐term memory (LSTM) and Autoformer architectures. LSTM handles short‐term changes in the data, and Autoformer focuses on long‐term trends via its autocorrelation property. After that, the outputs from both network branches are passed through fully connected (FC) layers and a final regression layer for precise prediction of load. The effectiveness of VMD‐TDLNet is evaluated using real‐world daily electricity consumption data from Kuwait over a 1‐year period, demonstrating its superior performance in terms of prediction accuracy, robustness to noise, and ability to generalize across temporal scales compared to baseline models. The proposed method reduces normalized mean absolute error (NMAE) by 10.1% compared to Transformer and 25.83% compared to LSTM in 1‐year forecasting. The method presents a strong and highly adjustable way to forecast loads in smart grid and energy management applications.

The transformation of the energy system towards volatile renewable generation, increases the need to manage decentralized flexibilities more efficiently. For this, precise forecasting of uncontrollable electrical load is key. Although there is an abundance of studies presenting innovative individual methods for load forecasting, comprehensive comparisons of popular methods are hard to come across.In this paper, eight methods for day-ahead forecasts of supermarket, school and residential electrical load on the level of individual buildings are compared. The compared algorithms came from machine learning and statistics and a median ensemble combining the individual forecasts was used.In our examination, nearly all the studied methods improved forecasting accuracy compared to the naïve seasonal benchmark approach. The forecast error could be reduced by up to 35% compared to the benchmark. From the individual methods, the neural networks achieved the best results for the school and supermarket buildings, whereas the k-nearest-neighbor regression had the lowest forecasting error for households. The median ensemble narrowly yielded a lower forecast error than all individual methods for the residential and school category and was only outperformed by a neural network for the supermarket data. However, this slight increase in performance came at the cost of a significantly increased computation time. Overall, identifying a single best method remains a challenge specific to the forecasting task.

In the energy-planning sector, the precise prediction of electrical load is a critical matter for the functional operation of power systems and the efficient management of markets. Numerous forecasting platforms have been proposed in the literature to tackle this issue. This paper introduces an effective framework, coded in Python, that can forecast future electrical load based on hourly or daily load inputs. The framework utilizes a recurrent neural network model, consisting of two simpleRNN layers and a dense layer, and adopts the Adam optimizer and tanh loss function during the training process. Depending on the size of the input dataset, the proposed system can handle both short-term and medium-term load-forecasting categories. The network was extensively tested using multiple datasets, and the results were found to be highly promising. All variations of the network were able to capture the underlying patterns and achieved a small test error in terms of root mean square error and mean absolute error. Notably, the proposed framework outperformed more complex neural networks, with a root mean square error of 0.033, indicating a high degree of accuracy in predicting future load, due to its ability to capture data patterns and trends.

Electrical load forecasting is a core element reflecting the operating conditions of the electricity system and a key tool responding to the demand of the electricity market. Achieving accurate short-term load forecasts remains a challenge due to the dynamic and non-stationary characteristics of the load data. Previous studies have mostly analyzed electrical load transformations from a single perspective. This approach often overlooks the dynamic diversity across different frequencies and the comprehensive effects of multi-time scale and granularity information. Research in electrical load forecasting has frequently failed to fully integrate multi-granularity perspectives. In this study, we introduce a novel approach, multi-granularity time-augmented learning (MTAL), to enhance the precision of short-term electrical load forecasting. Since the degree of dynamic change of different granularity information is overly influenced by time features, we design a time-augmented block to learn time representation and apply it to all granularity information to represent multi-granularity electrical load more reasonably. Furthermore, we incorporate an attention mechanism into the model, which serves to mitigate information redundancy and bolster its generalization capabilities. We evaluated our method on a univariate electrical load dataset and a multivariate electrical load dataset, respectively, and compared its performance with existing forecasting models. Experiments demonstrate that the MTAL model performs well in capturing load variation information and achieves better performance in both univariate and multivariate short-term electric load forecasting tasks. Compared to existing methods, our proposed model improves the prediction accuracy by 10 % and reduces the computation time by 18 % .

The article focuses on the findings of endurance tests on thrust bearings. In addition to the mechanical load (axial load: 10 ≤ C0/P ≤ 19, lubrication gap: 0.33 µm ≤ h0 ≤ 1.23 µm), these bearings are also exposed to electrical loads (voltage: 20 Vpp ≤ U0 ≤ 60 Vpp, frequency 5 kHz and 20 kHz), such as those generated by modern frequency converters. In a previous study, the focus was on the chemical change in the lubricant and the resulting wear particles. In contrast, this article focuses on the changes occurring in the metallic contact partners. Therefore, the changes in the surface topography are analysed using Abbott–Firestone curves. These findings show that tests with an additional electrical load lead to a significant reduction in roughness peaks. A correlation to acceleration measurements is performed. Moreover, it is shown that the electrical load possibly has an effect on the light load hardness. An increase in the occurring wear could not be detected during the test series. Also, a comparison with mechanical reference tests is made. The article finally provides an overview of different measurement values and their sensitivity to additional electrical loads in roller bearings.

Management and efficient operations in critical infrastructures such as smart grids take huge advantage of accurate power load forecasting, which, due to its non‐linear nature, remains a challenging task. Recently, deep learning has emerged in the machine learning field achieving impressive performance in a vast range of tasks, from image classification to machine translation. Applications of deep learning models to the electric load forecasting problem are gaining interest among researchers as well as the industry, but a comprehensive and sound comparison among different—also traditional—architectures is not yet available in the literature. This work aims at filling the gap by reviewing and experimentally evaluating four real world datasets on the most recent trends in electric load forecasting, by contrasting deep learning architectures on short‐term forecast (one‐day‐ahead prediction). Specifically, the focus is on feedforward and recurrent neural networks, sequence‐to‐sequence models and temporal convolutional neural networks along with architectural variants, which are known in the signal processing community but are novel to the load forecasting one.

The growing demand for consumer-end electrical load is driving the need for smarter management of power sector utilities. In today’s technologically advanced society, efficient energy usage is critical, leaving no room for waste. To prevent both electricity shortage and wastage, electrical load forecasting becomes the most convenient way out. However, the conventional and probabilistic methods are less adaptive to the acute, micro, and unusual changes in the demand trend. With the recent development of artificial intelligence (AI), machine learning (ML) has become the most popular choice due to its higher accuracy based on time-, demand-, and trend-based feature extractions. Thus, we propose an Extreme Gradient Boosting (XGBoost) regression-based model—PredXGBR-1, which employs short-term lag features to predict hourly load demand. The novelty of PredXGBR-1 lies in its focus on short-term lag autocorrelations to enhance adaptability to micro-trends and demand fluctuations. Validation across five datasets, representing electrical load in the eastern and western USA over a 20-year period, shows that PredXGBR-1 outperforms a long-term feature-based XGBoost model, PredXGBR-2, and state-of-the-art recurrent neural network (RNN) and long short-term memory (LSTM) models. Specifically, PredXGBR-1 achieves an mean absolute percentage error (MAPE) between 0.98 and 1.2% and an R2 value of 0.99, significantly surpassing PredXGBR-2’s R2 of 0.61 and delivering up to 86.8% improvement in MAPE compared to LSTM models. These results confirm the superior performance of PredXGBR-1 in accurately forecasting short-term load demand.

Reliability study is required for all the industrial applications to understand the behavior and functionality of device with respect to technological, environmental and operational issues. This paper aim to study and analyzed the exiting contribution related to MEMS resonator in order to explore the reliability issues. It also explored the reliability related to packaging, designing and functioning of MEMS resonator. It summarized the analysis of Environmental effect, design parameters, intermolecular forces, varying electrical load and vibrations etc. An approach and methodology for testing and simulation of MEMS resonator in view of reliability analysis is presented.

The reliability and efficiency of power system operations, especially in smart grid scenarios, depend on accurate load demand forecasting. Electrical load forecasting is crucial for power system design, fault protection and diversification as it reduces operating costs while enhancing the system’s overall reliability, stability, and efficiency from an economic and technical perspective. Previously, load forecasting analysis has frequently been limited by inadequate feature engineering and insufficient model tuning. Prediction reliability was reduced by many previous methods’ inabilities to accurately evaluate short-term variations over time and the impact of important variables. These constraints encouraged us to develop a more reliable and thorough forecasting procedure. This research proposes an enhanced short-term load forecasting framework based on a hyperparameter-tuned long short-term memory (LSTM) using a deep learning method recurrent neural network (RNN), alongside more neural network-based models such as artificial neural networks, k-nearest neighbors, and backpropagation neural networks. Hyperparameter optimization techniques (Keras Tuner, Grid SearchCV, Scikeras + Randomized SearchCV, etc.) were used to systematically tune training parameters, learning rates, and network architectures for each forecasting model to increase model accuracy. To provide a more reliable and accurate evaluation of forecasting performance, this research employs the use of an hourly load dataset (2003–2014) enhanced with historical and environmental variables. Significant statistical metrics, such as a mean absolute error of 0.0048, root mean squared error of 0.0091, coefficient of determination of R2 0.9958, and mean absolute percentage error of 1.60%, demonstrate that the hyperparameter optimized with hourly data performed better than both conventional and other deep learning models, with the highest efficiency of all tested models. In accordance with the results, accurate LSTM-RNN parameter modification significantly improves prediction accuracy.

Currently, deep learning methods have become prevalent in the field of electrical load forecasting. These approaches have shown a great potential to map complex nonlinear feature interactions. However, many existing electrical load forecasting models based solely on deep learning suffer from various limitations including the inability to perceive and integrate multi-scale features, the absence of continuous information of electrical load series and capturing fine-grained and hidden temporal pattern of electrical load series. In order to address these issues, in this paper we propose an improved model based on neural ordinary differential equations (NODEs), which possesses the ability of adaptive fusion, multi-scale feature perception, and representation. This model strengthens the effective decomposition of multi-scale features with the NODE-series block and enhances the multi-scale feature extraction and fusion ability with the NODE-split block. The experimental results show that the proposed model outperforms five baseline models and three block ablation experiments prove the necessity of the blocks.