Efficient use of deep learning and machine learning for load forecasting in South African power distribution networks

Abstract: Load forecasting, which is the act of anticipating future loads, has been shown to be important in power system network planning, operations and maintenance. Artificial Intelligence (AI) techniques have been shown to be good tools for load forecasting. Load forecasting can assist power distribution utilities maximise their revenue through optimising maintenance planning. With the dawn of the smart grid, first world countries have moved past the customer’s point of supply and use smart meters to forecast customer loads. These recent studies also utilise recent state of the art AI techniques such as deep learning techniques. Weather parameters are such as temperature, humidity and rainfall are usually used as parameters in these studies. South African load forecasting studies are outdated and recent studies are limited. Most of these studies are from 2010, and dating backwards to 1999. Hence they do not use recent state of the art AI techniques. The studies do not focus at distribution level load forecasting for optimal maintenance planning. The impact of adjusting power consumption data when there are spikes and dips in the data was not investigated in all these South African studies. These studies did not investigate the impact of weather parameters on different South African loads and hence load forecasting performance...D.Phil. (Electrical and Electronic Management

The foundation of capability modelling: A study of the impact and utilisation of human resources

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research aims at finding a foundation for assessment of capabilities and applying the concept in a human resource selection. The research identifies a common ground for assessing individuals’ applied capability in a given job based on literature review of various disciplines in engineering, human sciences and economics. A set of criteria is found to be common and appropriate to be used as the basis of this assessment. Applied Capability is then described in this research as the impact of the person in fulfilling job requirements and also their level of usage from their resources with regards to the identified criteria. In other words how their available resources (abilities, skills, value sets, personal attributes and previous performance records) can be used in completing a job. Translation of the person’s resources and task requirements using the proposed criteria is done through a novel algorithm and two prevalent statistical inference techniques (OLS regression and Fuzzy) are used to estimate quantitative levels of impact and utilisation. A survey on post graduate students is conducted to estimate their applied capabilities in a given job. Moreover, expert academics are surveyed on their views on key applied capability assessment criteria, and how different levels of match between job requirement and person’s resources in those criteria might affect the impact levels. The results from both surveys were mathematically modelled and the predictive ability of the conceptual and mathematical developments were compared and further contrasted with the observed data. The models were tested for robustness using experimental data and the results for both estimation methods in both surveys are close to one another with the regression models being closer to observations. It is believed that this research has provided sound conceptual and mathematical platforms which can satisfactorily predict individuals’ applied capability in a given job. This research has contributed to the current knowledge and practice by a) providing a comparison of capability definitions and uses in different disciplines, b) defining criteria for applied capability assessment, c) developing an algorithm to capture applied capabilities, d) quantification of an existing parallel model and finally e) estimating impact and utilisation indices using mathematical methods

The foundation of capability modelling : a study of the impact and utilisation of human resources

This research aims at finding a foundation for assessment of capabilities and applying the concept in a human resource selection. The research identifies a common ground for assessing individuals’ applied capability in a given job based on literature review of various disciplines in engineering, human sciences and economics. A set of criteria is found to be common and appropriate to be used as the basis of this assessment. Applied Capability is then described in this research as the impact of the person in fulfilling job requirements and also their level of usage from their resources with regards to the identified criteria. In other words how their available resources (abilities, skills, value sets, personal attributes and previous performance records) can be used in completing a job. Translation of the person’s resources and task requirements using the proposed criteria is done through a novel algorithm and two prevalent statistical inference techniques (OLS regression and Fuzzy) are used to estimate quantitative levels of impact and utilisation. A survey on post graduate students is conducted to estimate their applied capabilities in a given job. Moreover, expert academics are surveyed on their views on key applied capability assessment criteria, and how different levels of match between job requirement and person’s resources in those criteria might affect the impact levels. The results from both surveys were mathematically modelled and the predictive ability of the conceptual and mathematical developments were compared and further contrasted with the observed data. The models were tested for robustness using experimental data and the results for both estimation methods in both surveys are close to one another with the regression models being closer to observations. It is believed that this research has provided sound conceptual and mathematical platforms which can satisfactorily predict individuals’ applied capability in a given job. This research has contributed to the current knowledge and practice by a) providing a comparison of capability definitions and uses in different disciplines, b) defining criteria for applied capability assessment, c) developing an algorithm to capture applied capabilities, d) quantification of an existing parallel model and finally e) estimating impact and utilisation indices using mathematical methods.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design and Electronic Implementation of Machine Learning-based Advanced Driving Assistance Systems

200 p.Esta tesis tiene como objetivo contribuir al desarrollo y perfeccionamiento de sistemas avanzados a la conducción (ADAS). Para ello, basándose en bases de datos de conducción real, se exploran las posibilidades de personalización de los ADAS existentes mediante técnicas de machine learning, tales como las redes neuronales o los sistemas neuro-borrosos. Así, se obtienen parámetros característicos del estilo cada conductor que ayudan a llevar a cabo una personalización automatizada de los ADAS que equipe el vehículo, como puede ser el control de crucero adaptativo. Por otro lado, basándose en esos mismos parámetros de estilo de conducción, se proponen nuevos ADAS que asesoren a los conductores para modificar su estilo de conducción, con el objetivo de mejorar tanto el consumo de combustible y la emisión de gases de efecto invernadero, como el confort de marcha. Además, dado que esta personalización tiene como objetivo que los sistemas automatizados imiten en cierta manera, y siempre dentro de parámetros seguros, el estilo del conductor humano, se espera que contribuya a incrementar la aceptación de estos sistemas, animando a la utilización y, por tanto, contribuyendo positivamente a la mejora de la seguridad, de la eficiencia energética y del confort de marcha. Además, estos sistemas deben ejecutarse en una plataforma que sea apta para ser embarcada en el automóvil, y, por ello, se exploran las posibilidades de implementación HW/SW en dispositivos reconfigurables tipo FPGA. Así, se desarrollan soluciones HW/SW que implementan los ADAS propuestos en este trabajo con un alto grado de exactitud, rendimiento, y en tiempo real

Secure forecasting of user activities for distributed urban applications

Modelling human mobility is an interesting yet challenging research topic. Such mobility models can give valuable insight into user behavior. Such models can be used to forecast movement of people. Even though an interesting problem, it was not studied as widely due to lack of available mobility data. But modern communication and digital infrastructure has solved this problem. Thus, as a result, over the past decade and a half, this topic has attracted a lot of attraction. The modelling and forecasting of human mobility has widespread applications from transportation to advertisement. Such models can be used to in a collaborative manner to segment people or used in isolation to bring better services to an individual. Previous researches have presented different approaches for modelling human mobility. These range from neural networks to Markov chains. Some researchers have focused on location data while others have worked with accelerometer data. There are also recommendations to add more information to the data to understand the motive of mobility. This thesis approaches the problem of forecasting human mobility in the form of activities. GPS data is analyzed to mine information and find patterns. The forecasting is done in a twostep process. The first step is to analyze the data to identify and label activities, that are done on a routine basis. This is achieved by using an Adaptive Neuro-Fuzzy Inference System. This additional information helps understand the motive of moving from one place to another. In the second and final step the Markov Chain model is built for the movement among visited locations. The forecasting is done with respect to current time and location, keeping in view the motive of movement. The proposed system is implemented in JAVA and deployed as a combination of RESTful web services. Finally, accuracy tests are made on different datasets which show promising results

Secure forecasting of user activities for distributed urban applications

Modelling human mobility is an interesting yet challenging research topic. Such mobility models can give valuable insight into user behavior. Such models can be used to forecast movement of people. Even though an interesting problem, it was not studied as widely due to lack of available mobility data. But modern communication and digital infrastructure has solved this problem. Thus, as a result, over the past decade and a half, this topic has attracted a lot of attraction. The modelling and forecasting of human mobility has widespread applications from transportation to advertisement. Such models can be used to in a collaborative manner to segment people or used in isolation to bring better services to an individual. Previous researches have presented different approaches for modelling human mobility. These range from neural networks to Markov chains. Some researchers have focused on location data while others have worked with accelerometer data. There are also recommendations to add more information to the data to understand the motive of mobility. This thesis approaches the problem of forecasting human mobility in the form of activities. GPS data is analyzed to mine information and find patterns. The forecasting is done in a twostep process. The first step is to analyze the data to identify and label activities, that are done on a routine basis. This is achieved by using an Adaptive Neuro-Fuzzy Inference System. This additional information helps understand the motive of moving from one place to another. In the second and final step the Markov Chain model is built for the movement among visited locations. The forecasting is done with respect to current time and location, keeping in view the motive of movement. The proposed system is implemented in JAVA and deployed as a combination of RESTful web services. Finally, accuracy tests are made on different datasets which show promising results

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems