Electricity Forecasting For The Small Scale Power System Using Artificial Neural Network

The study is about to forecast the electricity demand values of UTP. The electricity profile of GDC (UTP) has been analyzed based on the historical data gathered. Using the analyzed data, forecast models have been developed prior to do forecasting. The models are being developed using Artificial Neural Network method. There are four models have been developed based on the conditions in UTP. Model 1 is developed to forecast for one week ahead for Semester OFF. Model 2 is developed to forecast for one week ahead for Semester ON. Furthermore, Model 3 and 4 are developed to forecast for 30 days ahead for Semester OFF and ON respectively. Upon developed the robust models, all the models have been simulated using five (5) different hidden neurons. As to obtained accurate forecasting result, the models have been simulated for twenty simulations for each of the hidden layer. From that, the error between forecasted and actual load have been obtained. From the result of the error calculation, the best forecast model is being chosen. Upon completing the project, the conclusion has been made based on the result from the forecasting as well as the values of MAPE

Development Of Artificial Bee Colony (Abc) Variants And Memetic Optimization Algorithms

Bio-inspired optimization algorithms (BIAs) have shown promising results in various diverse realms. One of BIAs, artificial bee colony (ABC) optimization algorithm, has shown excellent performance in many applications compared to other optimization algorithms. However, its performance sometimes deteriorates as the complexity of optimization problems increases. ABC normally has slow convergence rates on unimodal functions and yields premature convergence on complex multimodal functions. Researchers have proposed various ABC variants in order to overcome these problems. Nevertheless, the variants still fail to avoid both limitations simultaneously. Hence, this research work proposes six modified ABC variants and six memetic ABC algorithms with the aim of overcoming the problems of slow convergence rates and premature convergence. The modified ABC variants have been developed by inserting new processing stages into the standard ABC algorithm and modifying the employed-bees and onlooker-bees phases to balance out the exploration and exploitation capabilities of the algorithm. The proposed memetic ABC algorithms have been developed by hybridizing the proposed ABC variants with a local search technique, augmented evolutionary gradient search (EGS). The performances of all modified ABC variants and formulated memetic ABC algorithms have been evaluated on 27 benchmark functions. The best-performed modified ABC variants and memetic ABC algorithms are identified. To validate their robustness, the identified best-performed modified ABC variants and memetic ABC algorithms have been applied in three real-world applications; reactive power optimization (RPO), economic environmental dispatch (EED) and optimal digital IIR filter design. The obtained results have shown the superiority of the proposed optimization algorithms particularly JA-ABC5a, JA-ABC9 and EGSJAABC9 in comparison to the existing ABC variants and memetic ABC algorithm. For example, EGSJAABC9 has produced the most minimum power loss in comparison to other algorithms. Also, EGSJAABC9 has obtained the minimum EED value of 6.5593E+04 ($(lb)) for 6-generatior unit system while JA-ABC9 and EGSJAABC9 acquired the least EED value of 1.1656E+05 ($(lb)) for 10-generator unit system. Meanwhile, EGSJAABC9 has attained the best results at optimizing LP, BP and BS filters with 8.41E-03, 0.00E+00 and 5.70E-01 values of magnitude response error, respectively. As for optimizing HP filter, EGSJAABC9 is the second best. These results show that the proposed ABC variants and memetic ABC algorithms particularly EGSJAABC9 are robust optimization algorithms as they are able to converge faster and avoid premature convergence when dealing with complex optimization problems

Electricity Forecasting For The Small Scale Power System Using Artificial Neural Network

The study is about to forecast the electricity demand values of UTP. The electricity profile of GDC (UTP) has been analyzed based on the historical data gathered. Using the analyzed data, forecast models have been developed prior to do forecasting. The models are being developed using Artificial Neural Network method. There are four models have been developed based on the conditions in UTP. Modell is developed to forecast for one week ahead for Semester OFF. Model2 is developed to forecast for one week ahead for Semester ON. Furthermore, Model 3 and 4 are developed to forecast for 30 days ahead for Semester OFF and ON respectively. Upon developed the robust models, all the models have been simulated using five (5) different hidden neurons. As to obtained accurate forecasting result, the models have been simulated for twenty simulations for each of the hidden layer. From that, the error between forecasted and actual load have been obtained. From the result of the error calculation, the best forecast model is being chosen. Upon completing the project, the conclusion has been made based on the result from the forecasting as well as the values of MAPE

Evaluation of K-fold value in breast cancer diagnosis technique using SVM and bio-inspired optimization algorithm (JA-ABC5)

Breast cancer is a fatal condition that kills thousands of people annually and is becoming more common. Lowering the mortality rate linked to breast cancer requires early detection. On the other hand, screening tests like mammography, ultrasound, and MRI that rely on human interpretation run the risk of overdiagnosis or underdiagnosis. Classification techniques can be used to improve the accuracy of breast cancer diagnosis to get around this limitation. The purpose of this study is to determine how K-fold cross validation affects breast cancer classification performance. The K-fold value is crucial in determining the right value to use in order to speed up evaluation and guarantee consistency in the analysis. The study looks at how breast cancer identification accuracy is impacted by the K-fold value. The accuracy of the algorithmic performance estimation depends on K. Finding the right K value is crucial because a higher value of K produces an estimate that is more accurate but also costs more to compute. For practical classification performance analysis, a K-fold value of K5 is advised based on the Wisconsin dataset results. With an accuracy rate of 98.49% and an average completion time of 2677.823 seconds, this value showed superior robustness and completion time. This study emphasises the need for and value of K-fold cross-validation in enhancing the classification accuracy of breast cancer

New Enhanced Artificial Bee Colony (JA-ABC5) Algorithm with Application for Reactive Power Optimization

The standard artificial bee colony (ABC) algorithm involves exploration and exploitation processes which need to be balanced for enhanced performance. This paper proposes a new modified ABC algorithm named JA-ABC5 to enhance convergence speed and improve the ability to reach the global optimum by balancing exploration and exploitation processes. New stages have been proposed at the earlier stages of the algorithm to increase the exploitation process. Besides that, modified mutation equations have also been introduced in the employed and onlooker-bees phases to balance the two processes. The performance of JA-ABC5 has been analyzed on 27 commonly used benchmark functions and tested to optimize the reactive power optimization problem. The performance results have clearly shown that the newly proposed algorithm has outperformed other compared algorithms in terms of convergence speed and global optimum achievement

Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network

Development of a smart sensing unit for LoRaWAN-based IoT flood monitoring and warning system in catchment areas

This study introduces a novel flood monitoring and warning system (FMWS) that leverages the capabilities of long-range wide area networks (LoRaWAN) to maintain extensive network connectivity, consume minimal power, and utilize low data transmission rates. We developed a new algorithm to measure and monitor flood levels and rate changes effectively. The innovative, cost-effective, and user-friendly FMWS employs an HC-SR04 ultrasonic sensor with an Arduino microcontroller to measure flood levels and determine their status. Real-time data regarding flood levels and associated risk levels (safe, alert, cautious, or dangerous) are updated on The Things Network and integrated into TagoIO and ThingSpeak IoT platforms through a custom-built LoRaWAN gateway. The solar-powered system functions as a stand-alone beacon, notifying individuals and authorities of changing conditions. Consequently, the proposed LoRaWAN-based FMWS gathers information from catchment areas according to water level risks, triggering early flood warnings and sending them to authorities and residents via the mobile application and multiple web-based dashboards for proactive measures. The system's effectiveness and functionality are demonstrated through real-life implementation. Additionally, we evaluated the performance of the LoRa/LoRaWAN communication interface in terms of RSSI, SNR, PDR, and delay for two spreading factors (SF7 and SF12). The system's design allows for future expansion, enabling simultaneous data reporting from multiple sensor monitoring units to a server via a central gateway as a network

Cycle time minimization in production line using robust hybrid optimization algorithm

Bio-inspired algorithms that have been introduced by mimicking the biological phenomenon of nature have widely implemented to cater various real-world problems. As example, memetic algorithm, EGSJAABC3 is applied for economic environmental dispatch (EED) optimization, Hybrid Pareto Grey Wolf Optimization to minimize emission of noise and carbon in U-shaped robotic assembly line and Polar Bear Optimization to optimize heat production. The results obtained from their research have clearly portrayed the robustness of bio-inspired algorithms to cater complex problems. Assembly line, which is normally the last step of production that involves final assembly of the products. An assembly line generally consists of several workstations placed in sequential order. Each of the workstation is in charge to complete certain specific jobs. Hence, it is a must to make the best use of the efficiency of the assembly line. Cycle time minimization is part of the assembly line balancing problem due to its uncertainty that dependent on the number of manpower, material preparation and machine capacity. Cycle time basically means time needed to process a product using a specific task in a production line. This project proposes the application of new hybrid optimization algorithm named JAABC5-RRO to minimize cycle time to produce a new audio product on a production line in a production company

A Survey on LoRaWAN Technology: Recent Trends, Opportunities, Simulation Tools and Future Directions

Low-power wide-area network (LPWAN) technologies play a pivotal role in IoT applications, owing to their capability to meet the key IoT requirements (e.g., long range, low cost, small data volumes, massive device number, and low energy consumption). Between all obtainable LPWAN technologies, long-range wide-area network (LoRaWAN) technology has attracted much interest from both industry and academia due to networking autonomous architecture and an open standard specification. This paper presents a comparative review of five selected driving LPWAN technologies, including NB-IoT, SigFox, Telensa, Ingenu (RPMA), and LoRa/LoRaWAN. The comparison shows that LoRa/LoRaWAN and SigFox surpass other technologies in terms of device lifetime, network capacity, adaptive data rate, and cost. In contrast, NB-IoT technology excels in latency and quality of service. Furthermore, we present a technical overview of LoRa/LoRaWAN technology by considering its main features, opportunities, and open issues. We also compare the most important simulation tools for investigating and analyzing LoRa/LoRaWAN network performance that has been developed recently. Then, we introduce a comparative evaluation of LoRa simulators to highlight their features. Furthermore, we classify the recent efforts to improve LoRa/LoRaWAN performance in terms of energy consumption, pure data extraction rate, network scalability, network coverage, quality of service, and security. Finally, although we focus more on LoRa/LoRaWAN issues and solutions, we introduce guidance and directions for future research on LPWAN technologies

Comparative analysis in execution of machine learning in breast cancer identification: a review

Carcinoma known as breast cancer is a significant common cancer among women worldwide. In line with the global trends, it accounts for many new cancer cases and cancer-related deaths, giving it a substantial public health issue in today's culture. Early diagnosis is the most effective method to reduce the number of deaths in patients with breast cancer. Effective and early diagnosis of breast cancer ensure like mammography or biopsy to ensure the long-term survival of affected patients. Several conflicts arise in using traditional approaches, such as overdiagnosis or under-diagnosis. Machine learning is used to overcome the issues where it can strengthen the current conventional diagnosing of patients with breast cancer. The application of the classification method for diagnosing breast cancer is reviewed in this paper. Support Vector Machine (SVM), Naïve Bayes, K-Nearest Neighbour (KNN), Decision Tree, Artificial Neural Network (ANN), and logistic regression are six methods presented in the review. These techniques are integrated with conventional methods, often allow physicians to diagnose breast cancer effectively. In summary, machine learning improvises in diagnosing breast cancer in terms of accuracy, sensitivity, and specificity with excellent performance and quality of patients