Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

This paper introduces an effective Selective Harmonic Elimination (SHE) modulation technique in a five, seven, and nine-level cascaded H-bridge (CHB) multilevel inverter (MLI). Minimization of the harmonics and device counts is the basis for the ongoing research in the area of MLI. Reduced harmonics and hence the lower Total Harmonic Distortion (THD), improve the output power quality. SHE is a low-frequency modulation scheme to achieve this goal. SHE techniques are used to eliminate the distinct lower-order harmonics by determining the optimum switching angles. These angles are evaluated by solving the non-linear transcendental equations using any optimization technique. For this purpose, the Crystal Structure Algorithm (CryStAl) has been used in this paper. It is a metaheuristic, nature-inspired, and highly efficient optimization technique. CryStAl is a simple and parameter-free algorithm that doesn’t require the determination of any internal parameter during the optimization process. It is based on the concept of crystal structure formation by joining the basis and lattice point. This natural occurrence can be realized in crystalline minerals in their symmetrically organized components: ions, atoms, and molecules. The concept has been utilized to solve non-linear transcendental equations. SIMULINK/MATLAB environment has been used for the simulation. The simulation result shows that the crystal structure algorithm is very effective and excels the other metaheuristic algorithm. Hardware results validate the performance.

Recent research has focused on sustainable development and renewable energy resources, thus motivating nonconventional cutting-edge technology development. Multilevel inverters are cost-efficient devices with IGBT switches that can be used in ac power applications with reduced harmonics. They are widely used in the power electronics industry. However, under extreme stress, the IGBT switches can experience a fault, which can lead to undesirable operation. There is a need for a reliable system for detecting switch faults. This paper proposes a signal processing method to detect open-circuit problems in IGBT switches. Relative wavelet energy has been used as a feature for a machine learning algorithm to diagnose and classify the faulted switches. The switching sequence can be altered to restore a healthy output voltage. Inverter faults have been diagnosed by using support vector machine (SVM) and decision tree (DT), and an ensemble model based on decision tree (DT) and XG boost algorithm was developed, which yielded 92%, 88%, and 94.12% accuracy, respectively.

Input voltage control had been used for the improvement of power factor in grid-connected wind power based induction generators. However, efficiency in this case is very poor and the control range is also limited. The conventional rotor resistance control had also been used for wind power generation but in this case too, the input power factor becomes very poor. In this paper, a new control method is being proposed where both input voltage and slip power control are combined to achieve better performance of wound rotor induction generators (WRIG) for wind power generation. For each operating point, an optimum input voltage is set and the slip power is controlled by rotor impedance such that the maximum efficiency is achieved. Both power factor and efficiency improve for a wide range of speed variation. Moreover, the reactive power demand remains nearly constant which is compensated by a fixed value capacitor whose optimum value is found through the simulation. Moreover, the capacitors being placed at the rotor side, the efficiency of the proposed controller improves further. This scheme is useful for low power wind energy conversion system (WECS) where the wind speed varies over wide range. The performance of the proposed system under various wind conditions is experimentally verified.

Due to the strong correlation between symmetric frames, video signals have a high degree of temporal redundancy. Motion estimation techniques are computationally expensive and time-consuming processes used in symmetric video compression to reduce temporal redundancy. The block-matching technique is, on the other hand, the most popular and efficient of the different motion estimation and compensation techniques. Motion compensation based on the block-matching technique generally uses the minimization of either the mean square error (MSE) or mean absolute difference (MAD) in order to find the appropriate motion vector. This paper proposes to remove the highly temporally redundant information contained in each block of the video signal using the removing temporal redundancy (RTR) technique in order to improve the data rate and efficiency of the video signal. A comparison between the PSNR values of this technique and those of the JPEG video compression standard is made. As a result of its moderate memory and computation requirements, the algorithm was found to be suitable for mobile networks and embedded devices. Based on a detailed set of testing scenarios and the obtained results, it is evident that the RTR compression technique allowed a compression ratio of 22.71 and 95% loss in bit rate reduction while maintaining sufficient intact signal quality with minimized information loss.