Economic and environmental evaluation of renewable energy systems

The main objective of this thesis is to evaluate the economic and environmental effectiveness of three different renewable energy systems: solar PV, wind energy and biomass energy systems. Financial methods such as Internal Rate of Return (IRR) and Modified Internal Rate of Return (MIRR) were used to evaluate economic competitiveness. Seasonal variability in power generation capability of different renewable systems were also taken into consideration. In order to evaluate the environmental effectiveness of different energy systems, default values in GaBi software were taken by defining the functional unit as 1kWh. The results show that solar PV systems are difficult to justify both in economic as well as environmental grounds. Wind energy performs better in both economic and environmental grounds and has the capability to compete with conventional energy systems. Biomass energy systems exhibit environmental and economic performance at the middle level. In each of these systems, results vary.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Evaluation of the main energy scenarios for the global energy transition

Energy scenarios are used as a tool to examine credible future states and pathways. The one who constructs a scenario defines the framework in which the possible outcomes exist. The credibility of a scenario depends on its compatibility with real world experiences, and on how well the general information of the study, methodology, and originality and processing of data are disclosed. In the thesis, selected global energy scenarios’ transparency and desirability from the society’s point of view were evaluated based on literature derived criteria. The global energy transition consists of changes to social conventions and economic development in addition to technological development. Energy solutions are economic and ethical choices due to far-reaching impacts of energy decision-making. Currently the global energy system is mostly based on fossil fuels, which is unsustainable over the long-term due to various reasons: negative climate change impacts, negative health impacts, depletion of fossil fuel reserves, resource-use conflicts with water management and food supply, loss of biodiversity, challenge to preserve ecosystems and resources for future generations, and inability of fossil fuels to provide universal access to modern energy services. Nuclear power and carbon capture and storage cannot be regarded as sustainable energy solutions due to their inherent risks and required long-term storage. The energy transition is driven by a growing energy demand, decreasing costs of renewables, modularity and scalability of renewable technologies, macroeconomic benefits of using renewables, investors’ risk awareness, renewable energy related attractive business opportunities, almost even distribution of solar and wind resources on the planet, growing awareness of the planet’s environmental status, environmental movements and tougher environmental legislation. Many of the investigated scenarios identified solar and wind power as a backbone for future energy systems. The scenarios, in which the solar and wind potentials were deployed in largest scale, met best the set out sustainability criteria. In future research, energy scenarios’ transparency can be improved by better disclosure on who has ordered the study, clarifying the funding, clearly referencing to used sources and indicating processed data, and by exploring how variations in cost assumptions and deployment of technologies influence on the outcomes of the study.