Load forecasting is a key issue for efficient real-time energy management in smart grids. To control the load using demand side management accurately, load forecasting should be predicted in the short term. With the advent of advanced measuring infrastructure, it is possible to measure energy consumption at sampling rates up to every 5 min and analyze the load profile of small-scale energy groups, such as individual buildings. This paper presents applications of deep learning using feature decomposition for improving the accuracy of load forecasting. The load profile is decomposed into a weekly load profile and then decomposed into intrinsic mode functions by variational mode decomposition to capture periodic features. Then, a long short-term memory network model is trained by three-dimensional input data with three-step regularization. Finally, the prediction results of all intrinsic mode functions are combined with advanced measuring infrastructure measured in the previous steps to determine an aggregated output for load forecasting. The results are validated by applications to real-world data from smart buildings, and the performance of the proposed approach is assessed by comparing the predicted results with those of conventional methods, nonlinear autoregressive networks with exogenous inputs, and long short-term memory network-based feature decomposition.

Monitoring the aging battery cells is important to maintain battery performance. Capacity is a key indicator of battery aging diagnosis, but capacity can be estimated differently depending on the C-rate. Battery impedance monitoring using electrochemical impedance spectroscopy is also recognized as a battery diagnostic method. However, electrochemical impedance spectroscopy is inefficient when diagnosing a large number of individual battery cells due to complex computation and measurement time. In this paper, a harmonic analysis method for monitoring aging battery is proposed. The proposed diagnostic technique applies the designed 1-kHz sinusoidal signal, comprising a sinusoidal current component and a direct current component, to the battery. Harmonics are generated when signals passing through nonlinear systems are distorted. A battery is an asymmetric nonlinear system with different bi-directionality. Comparing the simulation and experimental results, we confirmed that the harmonic signatures are the indicators that distinguish between a normal and an aging battery. Moreover, the proposed in situ technique can be applied during charge/discharge cycles, even in the case of apparent reduction of even harmonics.

This paper presents a multiscale phasor measurement unit (PMU) data-compression method based on clustering analysis of wide-area power systems. PMU data collected from wide-area power systems involve local characteristics that are significant risk factors when applying dimensionality-reduction-based data compression. Therefore, density-based spatial clustering of applications with noise (DBSCAN) is proposed for the preconditioning of PMU data, except for bad data and the automatic segmentation of correlated local datasets. Clustered PMU datasets of a local area are then compressed using multiscale principal component analysis (MSPCA). When applying MSPCA, each PMU signal is decomposed into frequency sub-bands using wavelet decomposition, approximation matrix, and detail matrices. The detail matrices in high-frequency sub-bands are compressed by using a PCA-based linear-dimensionality reduction process. The effectiveness of DBSCAN for data compression is verified by application of the proposed technique to the real-world PMU voltage and frequency data. In addition, comparisons are made with existing compression techniques in wide-area power systems.

Renewable energy systems are essential for carbon neutrality and energy savings in industrial facilities. Factories use a lot of electrical and thermal energy to manufacture products, but only a small percentage is recycled. Utilizing energy storage systems in industrial facilities is being applied as a way to cut energy costs and reduce carbon emissions. However, lithium-based batteries, which are predominantly used in traditional industries, face challenges in terms of affordability and reliability. Redox flow batteries, on the other hand, offer high power output and reliability, and are economical to manufacture for installations with high capacity. Although redox flow batteries are difficult to use in general electrical systems due to their small volume-to-capacity ratio, they can be easily utilized as energy storage devices in industrial parks or renewable energy parks with relatively little space constraints. In addition, since factories use a lot of heat energy in addition to electricity, utilizing combined heat and power can further reduce heat energy. In this study, we analyzed the cost estimation and economic feasibility of utilizing photovoltaics, redox flow cells, and combined heat and power to save energy in a factory’s energy management system.

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions.