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Research Article
Efficiency of Multispectral Pyrometer Technology in the Infrared Spectral Band According to Planck's Law on the Real Body in the Case of Oxidized Steels
Issue:
Volume 14, Issue 3, June 2026
Pages:
129-134
Received:
16 April 2026
Accepted:
27 April 2026
Published:
11 May 2026
DOI:
10.11648/j.jeee.20261403.11
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Abstract: This research is based on a theoretical analysis of thermal radiation, the fundamental laws of Planck and Kirchhoff, and multispectral processing methods using nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific and industrial fields, including thermal processes, metallurgy, energy, and fundamental research. Planck’s law gives de relation between radiation, temperature ant wavelength. That low can be used to determine temperature by pyrometry method. Besides Planck’s law for the black body multiplied by the emissivity will gives the expression of the real body. Uncertainty in emissivity is the main source of error in conventional pyrometric measurements. In our case, the polynomial model of emissivity only goes up to the second order. To reduce the influence of emissivity and improve measurement reliability, several approaches have been developed, including monochromatic, bichromatic, and multispectral pyrometry based on Planck's law. The characteristics of chromatic luminance in the near and mid-infrared bands highlight the high potential of pyrometry for measuring high temperatures in complex environments. Compared to traditional approaches, this pyrometry technology offers greater robustness to variations in emissivity and environmental uncertainties. Luminance across the infrared spectral band and a temperature range provides improved linearity. This linearity highlights the strength of using infrared radiation for remote temperature sensing. The mid-infrared zone offers greater stability and a closer relationship between luminance, temperature, and wavelength in temperature detection for oxidized steel.
Abstract: This research is based on a theoretical analysis of thermal radiation, the fundamental laws of Planck and Kirchhoff, and multispectral processing methods using nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific and industrial fields, including thermal processes, metallurgy, energy, and fundamental resear...
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Research Article
Limitations of Pyrometer Technology in the Ultraviolet and Visible Spectral Bands According to Planck's Law on the Real Body
Ratianarivo Paul Ezekel*
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Rastefano Elisee
Issue:
Volume 14, Issue 3, June 2026
Pages:
135-142
Received:
23 April 2026
Accepted:
6 May 2026
Published:
14 May 2026
DOI:
10.11648/j.jeee.20261403.12
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Abstract: This article investigates the limitations of pyrometric technology in the visible and ultraviolet spectral bands. These studies rely on a theoretical analysis of thermal radiation, Planck's fundamental laws, and multispectral processing methods based on nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific and industrial fields, including thermal processes, metallurgy, energy, and fundamental research. Uncertainty in emissivity is the main source of error in conventional pyrometric measurements. To reduce the influence of emissivity and improve measurement reliability, several approaches have been developed, including monochromatic, bichromatic, and multispectral pyrometry based on Planck's law. The chromatic luminance characteristics in the visible and ultraviolet bands, obtained from a temperature range across both spectral domains, highlight the high potential of pyrometry for measuring high temperatures in complex environments. These characteristics will be applied with different wavelengths in each visible and ultraviolet spectral band. Comparative studies of the results will be able to highlight the limitations for each band. Compared to traditional approaches, this pyrometry technology offers a small advantage for detecting very high temperatures despite variations in emissivity and environmental uncertainties. The luminance for these two spectral bands exhibits a very low flux almost at temperatures below 1900K.
Abstract: This article investigates the limitations of pyrometric technology in the visible and ultraviolet spectral bands. These studies rely on a theoretical analysis of thermal radiation, Planck's fundamental laws, and multispectral processing methods based on nonlinear models. Accurate high-temperature measurement is a major challenge in many scientific ...
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Research Article
Data-Driven Load Forecasting and Power Flow Optimization Using Deep LSTM Networks
Randriamora Edmond*,
Rasolofonirina Tokiniaina Francky,
Randriamaroson Mahandrisoa Rivo
Issue:
Volume 14, Issue 3, June 2026
Pages:
143-151
Received:
1 April 2026
Accepted:
11 April 2026
Published:
16 May 2026
DOI:
10.11648/j.jeee.20261403.13
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Abstract: In light of the rapidly increasing global demand for energy, accurately forecasting short-term electricity consumption has become a critical yet challenging task for modern power system operation and planning, as traditional methods often struggle to handle the variability and uncertainty of load demand. To address these limitations, this study proposes an integrated and data-driven framework that combines an Alternating Current Optimal Power Flow (ACOPF) model with a Long Short-Term Memory (LSTM) recurrent neural network in order to simultaneously enhance forecasting accuracy and operational efficiency. The LSTM model, trained on historical load data, is used to generate reliable 24-hour ahead electricity demand forecasts, which are then dynamically incorporated into the MATPOWER simulation environment using the IEEE 30-bus test system. The results demonstrate that the proposed approach achieves a high level of predictive performance, with a root mean square error (RMSE) of 0.3794, indicating its effectiveness in capturing temporal load patterns. More importantly, the integration of these forecasts into the ACOPF framework enables more proactive and informed decision-making in power system operations, leading to a significant improvement in economic dispatch by reducing the hourly generation cost from $576.89/h under conventional approaches to $490.91/h, corresponding to an approximate cost reduction of 14.9%. Overall, this study highlights the strong synergy between deep learning techniques and optimization models for smart grid management, showing that the proposed framework not only improves forecasting precision but also enhances system efficiency, reduces operational costs, and supports more reliable, flexible, and cost-effective power system operations.
Abstract: In light of the rapidly increasing global demand for energy, accurately forecasting short-term electricity consumption has become a critical yet challenging task for modern power system operation and planning, as traditional methods often struggle to handle the variability and uncertainty of load demand. To address these limitations, this study pro...
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Research Article
Robust Power System Optimization Using LSTM-Based Load Forecasting and AIS Algorithm
Randriamora Edmond*,
Rasolofonirina Tokiniaina Francky,
Andriatsihoarana Harlin Samuel,
Randriamaroson Rivo Mahandrisoa
Issue:
Volume 14, Issue 3, June 2026
Pages:
152-162
Received:
3 April 2026
Accepted:
16 April 2026
Published:
16 May 2026
DOI:
10.11648/j.jeee.20261403.14
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Abstract: Optimizing alternating current (AC) power flow under uncertainty remains a major challenge in modern power systems, particularly with the increasing penetration of variable renewable energy sources. This paper proposes a hybrid two-stage framework that integrates long short-term memory (LSTM) networks for load forecasting with an artificial immune system (AIS)-based optimization approach, embedded within a Monte Carlo simulation scheme to explicitly account for uncertainty. The methodology is validated on the IEEE 30-bus test system. In the first stage, the LSTM model captures temporal dependencies to generate short-term load forecasts, while in the second stage, these forecasts are incorporated into an AIS-based AC optimal power flow (AC-OPF) formulation. Monte Carlo simulations are employed to model stochastic variations and assess system performance across multiple scenarios. The results show that, although the reduction in operational cost is relatively marginal compared to deterministic approaches, the proposed framework significantly enhances the robustness and stability of OPF solutions under forecasting uncertainty, improving the system’s ability to maintain feasible and consistent operating points despite variability in load predictions. However, the forecasting performance of the LSTM model is sensitive to noise and out-of-distribution inputs, which may affect the overall optimization quality. Overall, the main contribution of this work lies in the development of an integrated forecasting–optimization framework that strengthens the reliability and resilience of power system operation under uncertainty.
Abstract: Optimizing alternating current (AC) power flow under uncertainty remains a major challenge in modern power systems, particularly with the increasing penetration of variable renewable energy sources. This paper proposes a hybrid two-stage framework that integrates long short-term memory (LSTM) networks for load forecasting with an artificial immune ...
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