Integration of Distributed Renewable Generation (DRG) to the future Smart Grid (SG) is one of the important considerations that is highly prioritized in the SG development roadmap by most of the countries including Malaysia. The plausible way of this integration is the enhancement of information and bidirectional communication infrastructure for energy monitoring and controlling facilities. However, urgency of data delivery through maintaining critical time condition is not crucial in these facilities. In this paper, we have surveyed state-of-the-art protocols for different Wireless Sensor Networks (WSNs) with the aim of realizing communication infrastructure for DRG in Malaysia. Based on the analytical results from surveys, data communication for DRG should be efficient, flexible, reliable, cost effective, and secured. To meet this achievement, IEEE802.15.4 supported ZigBee PRO protocol together with sensors and embedded system is shown as Wireless Sensor (WS) for DRG bidirectional network with prospect of attaining data monitoring facilities. The prospect towards utilizing ZigBee PRO protocol can be a cost effective option for full integration of intelligent DRG and small scale Building-Integrated Photovoltaic (BIPV)/Feed-in-Tariff (FiT) under SG roadmap (Phase4: 2016–2017) conducted by Malaysia national utility company, Tenaga Nasional Berhad (TNB). Moreover, we have provided a direction to utilize the effectiveness of ZigBee-WS network with the existing optical communication backbone for data importing from the end DRG site to the TNB control center. A comparative study is carried out among developing countries on recent trends of SG progress which reveals that some common projects like smart metering and DRG integration are on priority

This study analyzes the performance of two PV modules, amorphous silicon (a-Si) and crystalline silicon (c-Si) and predicts energy yield, which can be seen as facilitation to achieve the target of 35% reduction of greenhouse gases emission by 2030. Malaysia Energy Commission recommends crystalline PV modules for net energy metering (NEM), but the climate regime is a concern for output power and efficiency. Based on rainfall and irradiance data, this study aims to categorize the climate of peninsular Malaysia into rainy and dry seasons; and then the performance of the two modules are evaluated under the dry season. A new mathematical model is developed to predict energy yield and the results are validated through experimental and systematic error analysis. The parameters are collected using a self-developed ZigBeePRO-based wireless system with the rate of 3 samples/min over a period of five days. The results unveil that efficiency is inversely proportional to the irradiance due to negative temperature coefficient for crystalline modules. For this phenomenon, efficiency of c-Si (9.8%) is found always higher than a-Si (3.5%). However, a-Si shows better shadow tolerance compared to c-Si, observed from a lesser decrease rate in efficiency of the former with the increase in irradiance. Due to better spectrum response and temperature coefficient, a-Si shows greater performance on output power efficiency (OPE), performance ratio (PR), and yield factor. From the regression analysis, it is found that the coefficient of determination (R²) is between 0.7179 and 0.9611. The energy from the proposed model indicates that a-Si yields 15.07% higher kWh than c-Si when luminance for recorded days is 70% medium and 30% high. This study is important to determine the highest percentage of energy yield and to get faster NEM payback period, where as of now, there is no such model to indicate seasonal energy yield in Malaysia.

The economic landscape of the United Kingdom has been significantly shaped by the intertwined issues of Brexit, COVID-19, and their interconnected impacts. Despite the country’s robust and diverse economy, the disruptions caused by Brexit and the COVID-19 pandemic have created uncertainty and upheaval for both businesses and individuals. Recognizing the magnitude of these challenges, academic literature has directed its attention toward conducting immediate research in this crucial area. This study sets out to investigate key economic factors that have influenced various sectors of the UK economy and have broader economic implications within the context of Brexit and COVID-19. The factors under scrutiny include the unemployment rate, GDP index, earnings, and trade. To accomplish this, a range of data analysis tools and techniques were employed, including the Box-Jenkins method, neural network modeling, Google Trend analysis, and Twitter-sentiment analysis. The analysis encompassed different periods: pre-Brexit (2011-2016), Brexit (2016-2020), the COVID-19 period, and post-Brexit (2020-2021). The findings of the analysis offer intriguing insights spanning the past decade. For instance, the unemployment rate displayed a downward trend until 2020 but experienced a spike in 2021, persisting for a six-month period. Meanwhile, total earnings per week exhibited a gradual increase over time, and the GDP index demonstrated an upward trajectory until 2020 but declined during the COVID-19 period. Notably, trade experienced the most significant decline following both Brexit and the COVID-19 pandemic. Furthermore, the impact of these events exhibited variations across the UK’s four regions and twelve industries. Wales and Northern Ireland emerged as the regions most affected by Brexit and COVID-19, with industries such as accommodation, construction, and wholesale trade particularly impacted in terms of earnings and employment levels. Conversely, industries such as finance, science, and health demonstrated an increased contribution to the UK’s total GDP in the post-Brexit period, indicating some positive outcomes. It is worth highlighting that the impact of these economic factors was more pronounced on men than on women. Among all the variables analyzed, trade suffered the most severe consequences in the UK. By early 2021, the macroeconomic situation in the country was characterized by a simple dynamic: economic demand rebounded at a faster pace than supply, leading to shortages, bottlenecks, and inflation. The findings of this research carry significant value for the UK government and businesses, empowering them to adapt and innovate based on forecasts to navigate the challenges posed by Brexit and COVID-19. By doing so, they can promote long-term economic growth and effectively address the disruptions caused by these interrelated issues.

Removal of noises from ECG (Electrocardiogram) signal is a classical problem. Moreover, nullifying AC and DC noises using the two adaptive algorithms-the LMS and the RLS from the ECG is a new study in biomedical science. In this paper, the four types of AC and DC noises have been implemented according to their basic properties. After that, these noises have been mixed with ECG signal and nullify these noises using the LMS and the RLS algorithms. At the end of this paper, a performance study has been done between these algorithms based on their parameters and also discussed the effect of filter length and the corresponding correlation coefficient. Results indicate that the DC bias noises cannot be handled by the LMS filtering whereas the RLS can handle both types of noises. Also, it is true for both algorithms that the filter length is proportional to MSE (Mean Square Error) rate and it takes more time to converge for both algorithms. Furthermore, most of the cases the RLS has achieved best effective noise cancellation performance although its convergence time is slightly high. But eventually its error has always dipped down below that of the LMS algorithm.

Artificial electromagnetic metamaterial produces exotic resonance, extra ordinary characteristics not available in nature, but engineers can inherit the characteristics by controlling and manipulating their structure. This research primarily the design and realization of dual-band, polarization, and incident angle insensitive metamaterial absorber (MA) is presented. By controlling and manipulating the electromagnetic design shape, artificial structure, periodic array pattern, and dielectric layer thickness a significant way to realize high absorption. In order to achieve high absorption a new shape of an octagonal ring (OR), cross-wires (CWs), and cut-off circle (CC) artificial structure have been sensibly selected. The special characteristics of this structure produce a dual resonance and its bandwidth rises compared that of classical absorber. The proposed artificial structure operation suits the Ku-band application, but possible to enhance in $C$ -band. The numerical and experimental results display a dual-band 99.8% at 12.2 GHz and 99.9% at 15.5 GHz resonance is an excellent agreement in theory and numerical analysis. The effects of the constitutive property parameters: dielectric constant ( $\varepsilon $ ), magnetic permeability ( $\mu$ ), and negative refractive index ( $n$ ) are also investigated. The investigation of symmetric design structure shows the polarization-insensitivity and high absorption initiated even in changing the incident angle. Numerical and experimental results confirmed the destructive interference of multiple penetrations are responsible for the near-unity absorption. An excellent agreement in the absorptivity rates that touches near perfection which is prominent for solar cells, detection, and imaging applications.

The project aims to develop a sustainable smart irrigation system (SIS) for the indoor plant irrigation by integrating photovoltaic (PV), internet of things (IoT), and rainwater harvesting techniques. The addressed problem involves the inconsistency and tediousness of manual watering, emphasizing the need for a sustainable design for a SIS. The IoT system consists of soil moisture sensor with GSM module powered by PV and an algorithm was developed to adjust irrigation schedules based on soil moisture data. The objectives of this project are to design and optimize the PV-powered irrigation system and implement an Arduino-enabled automatic system with SMS-triggered functionality. The methodology involves system modelling for water requirements and sizing of PV, battery, pump, and MPPT based on the load demand. The rainwater harvesting structure designed ensures water sustainability for plants’ irrigation. The system is then implemented using moisture and ultrasonic sensors managed by Arduino Uno embedded system. The electrical performance of the PV was analyzed on both cloudy and moderately luminous days, with irradiance ranging from 250.4 to 667.8 and 285.5 to 928 W/m², respectively. The average output voltage and current of the battery were observed to be 13.04 V and 0.37 A (cloudy), and 13.45 V and 0.47 A (moderate) days, respectively. The rainwater collection test revealed more than 36 L in the tank after one week, indicating it could sustain watering the three plants for 72 days. Based on the analysis, the project can save 14.97 kgCO₂ emissions per year compared to the current emissions released into the environment. The overall cost of the system is approximately RM670 (US$139.50). The SIS aligns with SDG 7, promoting affordable and integrates with 12^th Malaysia Plan for more efficient and environmentally friendly agricultural and water management practices.