Load forecasting (LF) has become the main concern in decentralized power generation systems with the smart grid revolution in the 21st century. As an intriguing research topic, it facilitates generation systems by providing essential information for load scheduling, demand-side integration, and energy market pricing and reducing cost. An intelligent LF model of residential loads using a novel machine learning (ML)-based approach, achieved by assembling an integration strategy model in a smart grid context, is proposed. The proposed model improves the LF by optimizing the mean absolute percentage error (MAPE). The time-series-based autoregression schemes were carried out to collect historical data and set the objective functions of the proposed model. An algorithm consisting of seven different autoregression models was also developed and validated through a feedforward adaptive-network-based fuzzy inference system (ANFIS) model, based on the ML approach. Moreover, a binary genetic algorithm (BGA) was deployed for the best feature selection, and the best fitness score of the features was obtained with principal component analysis (PCA). A unique decision integration strategy is presented that led to a remarkably improved transformation in reducing MAPE. The model was tested using a one-year Pakistan Residential Electricity Consumption (PRECON) dataset, and the attained results verify that the proposed model obtained the best feature selection and achieved very promising values of MAPE of 1.70%, 1.77%, 1.80%, and 1.67% for summer, fall, winter, and spring seasons, respectively. The overall improvement percentage is 17%, which represents a substantial increase for small-scale decentralized generation units.

Price forecasting (PF) is the primary concern in distributed power generation. This paper presents a novel and improved technique to forecast electricity prices. The data of various power producers, Capacity Purchase Price (CPP), Power Purchase Price (PPP), Tariff rates, and load demand from National Electric Power Regulatory Authority (NEPRA) are considered for MAPE reduction in PF. Eight time-series and auto-regression algorithms are developed for data fetching and setting the objective function. The feed-forward ANFIS based on the ML approach and space vector regression (SVR) is introduced to PF by taking the input from time series and auto-regression (AR) algorithms. Best-feature selection is conducted by adopting the Binary Genetic Algorithm (BGA)-Principal Component Analysis (PCA) approach that ultimately minimizes the complexity and computational time of the model. The proposed integration strategy computes the mean absolute percentage error (MAPE), and the overall improvement percentage is 9.24%, which is valuable in price forecasting of the energy management system (EMS). In the end, EMS based on the Firefly algorithm (FA) has been presented, and by implementing FA, the cost of electricity has been reduced by 21%, 19%, and 20% for building 1, 2, and 3, respectively.

This article presents an optimized microwave sensor for the non-contact measurement of complex permittivity and material thickness. The layout of the proposed sensor comprises the parallel combination of an interdigital capacitor (IDC) loaded at the center of the symmetrical differential bridge-type inductor fabricated on an RF-35 substrate (ε_r = 3.5 and tanδ = 0.0018). The bridge-type differential inductor is introduced to obtain a maximum inductance value with high quality (Q) factor and low tunable resonant frequency. The central IDC structure is configured as a spur-line structure to create a high-intensity coupled electric field (e-field) zone, which significantly interacts with the materials under test (MUTs), resulting in an increased sensitivity. The proposed sensor prototype with optimized parameters generates a resonant frequency at 1.38 GHz for measuring the complex permittivity and material thickness. The experimental results indicated that the resonant frequency of the designed sensor revealed high sensitivities of 41 MHz/mm for thickness with a linear response (r² = 0.91567), and 53 MHz/Δε_r for permittivity with a linear response (r² = 0.98903). The maximum error ratio for measuring MUTs with a high gap of 0.3 mm between the testing sample and resonator is 6.52%. The presented performance of the proposed sensor authenticates its application in the non-contact measurement of samples based on complex permittivity and thickness.

The demand for uninterruptible electricity supply is increasing, and the power engineering sector has started researching alternative solutions. Distributed generation (DG) dissemination into the electric grid to cope with the accelerating demand for electricity is taken into consideration. However, its integration with the traditional grid is a key task as sudden changes in load and their fickle nature cause the frequency to deviate from its adjusted range and affect the grid’s reliability. Moreover, the increased use of DG will significantly impact power system frequency response, posing a new challenge to the traditional power system frequency framework. Therefore, maintaining the frequency within the nominal range can improve its reliability. This deviation should be removed within a few seconds to keep the system’s frequency stable so that supply and demand are balanced. In a traditional grid system, the controllers installed at the generation side help to control the system’s frequency. These generators have capital installation costs that are not desirable for system operators. Therefore, this article proposed a comprehensive control framework to enable high penetration of DG while still providing adequate frequency response. This is accomplished by investigating a grasshopper optimization algorithm-based (GOA) fuzzy PD-PI controller (FPD-PI) for analyzing frequency control and optimizing the FPD-PI controller gains to minimize the frequency fluctuations. In this paper, interconnected hybrid power systems (HPS) are considered. In this study, the response of a system is analyzed, and the results validate that the oscillations in frequency are substantially reduced by the proposed controller. Moreover, our model is the best option for controlling frequency instead of conventional controllers, as it is efficient and fast to regulate frequency by switching the preferred loads on or off.