Electrical capacitance tomography (ECT) exhibits several attractive features that are important for industrial process tomography applications. These features include low cost, high speed, and nonintrusive nature. However, due to its soft-field character, a relatively low image resolution is an outstanding challenge for ECT. While many efforts have been made to tackle this challenge by improvements in image reconstruction algorithms, relatively less has been done to enhance the basic ECT hardware sensor configuration and data acquisition process. In this paper, a new measurement acquisition strategy is introduced to increase image resolution when using adaptive ECT (AECT). The proposed strategy is based on the manipulation of synthetic electrodes plates (``metaplates'') formed by a set of combined smaller physical electrodes (segments). The synthetic electrodes are sequentially activated with partial overlap of constituent segments to provide a fine-stepped axial and/or azimuthal electronic scan along the entire sensor. Consequently, an increased number of independent capacitance measurements are made available. Reconstruction results using the proposed measurement acquisition strategy (AECT) are shown to illustrate the enhanced resolution and stability in the imaging of objects compared to conventional ECT sensors.

We present a Relevance Vector Machine (RVM) based algorithm for electrical capacitance tomography (ECT) applications that can concurrently provide image reconstruction results and uncertainty estimates about the reconstruction. To illustrate the RVM operation in ECT, we simulate typical ECT scenarios, making explicit the connection between the reconstructed pixel values and the corresponding uncertainty estimates in each case. We compare the RVM reconstruction performance with that of the Iterative Landweber Method (ILM) and the least absolute shrinkage and selection operator (LASSO) in all the considered scenarios. The results show that, in addition to the key advantage of providing uncertainty measures, RVM can achieve similar reconstruction results with either lower or similar computational complexity.

Electrical capacitance tomography (ECT) is a noninvasive and non-intrusive imaging modality that utilizes mutual capacitance measurements between electrode plates to reconstruct the cross-sectional spatial electrical permittivity distribution inside a region of interest (RoI). Increasing attention has been given in recent years to the extension of ECT to volumetric imaging, also known as electrical capacitance volume tomography (ECVT). Most of the desirable properties of ECT carry over to ECVT. However, the speed of reconstruction is reduced in ECVT due to an increase in the number of measurements and in the number of voxels used to represent the permittivity distribution in the RoI. In this work we adopt a Fourier basis representation as a means to accelerate the speed of image reconstruction in ECVT. By considering only spatial frequency components below certain threshold, we show that the number of unknowns can be greatly reduced and the overall reconstruction process significantly accelerated. We present both numerical and experimental results to corroborate our findings.

Electrical capacitance tomography is a widely used sensor modality for flow imaging in many industrial settings. Adaptive electrical capacitance volume tomography (AECVT) extends the capabilities of traditional ECT by enabling direct volumetric imaging and an improved resolution. Construction of the sensitivity matrix is a necessary step to obtain flow images. This step requires the computation of the electric field inside the sensing domain, which is done via a typical field solver, such as the finite-element method. In this work, we present an efficient and flexible method to construct the sensitivity matrix for AECVT based on individual electrode segment excitations and their judicious combination to form desired matrix elements. We illustrate how the proposed method yields the same sensitivity matrix as the traditional method but at a much lower computational cost. Once all segment contributions are obtained, we also indicate how the proposed method, unlike the traditional approach, can generate the sensitivity matrix on demand for an arbitrary combination of synthetic electrodes and obviating the need for any additional field computations. Finally, we present image reconstruction results for two different experimental scenarios where the mutual capacitance data and the corresponding sensitivity vectors are obtained through the proposed measurement combination scheme.

Accurate load forecasting is essential for optimizing microgrid and smart grid operations, thereby supporting Energy Management Systems (EMSs). Load forecasting also plays a key role in integrating renewable energy, ensuring grid stability, and facilitating decision-making. In this regard, we present a comprehensive literature review that combines both bibliometric analysis and critical literature synthesis to evaluate state-of-the-art forecasting techniques. Based on a screened corpus of over 200 scientific publications from 2015 to 2024, our analysis reveals a significant shift in the field: AI-based approaches, including Machine Learning (ML) and Deep Learning (DL), represent more than 55% of the analyzed literature, overtaking traditional statistical models. The bibliometric results highlight a 300% increase in publications focusing on ML-based models (e.g., SVM, CNN, LSTM) over the years. Furthermore, approximately 70% of the total reviewed works use at least one exogenous variable, such as weather variables, socioeconomic indicators, and cultural behavior. These findings reflect the transition from traditional statistical models to more flexible and scalable approaches. However, socioeconomic and cultural variables remain underutilized in the literature, particularly for long-term planning. Despite the progress load forecasting processes have made in recent years, thanks to advanced modeling, a few hurdles remain to realizing their full potential in modern microgrids. Thus, we argue that future research should focus on three key areas: (i) scalable real-time adaptive models, including computational complexity characterization, (ii) standardization in data collection for seamless integration of exogenous variables, and (iii) real-world application of forecasting models in decision-making that supports EMSs. Progress in these areas may enhance grid stability, optimize resource allocation, and accelerate the transition to sustainable energy systems.

We introduce an efficient synthetic electrode selection strategy for use in Adaptive Electrical Capacitance Volume Tomography (AECVT). The proposed strategy is based on the Adaptive Relevance Vector Machine (ARVM) method and allows to successively obtain synthetic electrode configurations that yield the most decrease in the image reconstruction uncertainty for the spatial distribution of the permittivity in the region of interest. The problem is first formulated as an instance of the Quadratic Unconstrained Binary Optimization (QUBO). By noting that the QUBO formulation is an NP-hard problem and thus prohibitive in practice, we then introduce the Reduced ARVM method, corresponding to the application of the ARVM method to a reduced search space. By using the Reduced ARVM method, good image reconstruction and low uncertainty levels can be achieved in AECVT with considerably fewer measurements. To corroborate our analysis, we present simulation results for three representative AECVT scenarios.