tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario.
Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems

Experts from industry and academics have highlighted Additive Manufacturing (AM) as a technology that is revolutionizing manufacturing. AM is a process that consists of creating a three-dimensional object by incorporating layers of a material such as metal or polymer. This research studies risks associated with AM R&D Project Management. A significant set of risks with a potential negative impact on project objectives in terms of scope, schedule, cost and quality are identified through an extensive literature review. These risks are assessed through a survey answered by ninety academics and professionals with noteworthy sector expertise. This process is made by the measurement of two parameters: likelihood of occurrence and impact on project objectives. According to the responses of the experts, the level of relevance of each risk is calculated, innovatively, through a fuzzy logic-based model, specifically developed for this study, implemented in MATLAB Fuzzy Logic Toolbox. The results of this study show that the risks “Defects occurring during the manufacturing process”, “Defective design”, “Poor communication in the project team” and “Insufficient financing” are determined as the most critical in AM R&D Project Management. The proposed model is presented as a powerful new tool for organizations and academics, to prioritize the risks that are more critical to develop appropriate response strategies to achieve the success of their projects.
Keywords: Additive Manufacturing | 3D printing | Risk Assessment | Project Management | Fuzzy Logic

The field of Artificial Intelligence (AI) is enjoying unprecedented success and is dramatically transforming the landscape of the financial services industry. However, there is a strong need to develop an accountability and explainability framework for AI in financial services, based on a risk-based assessment of appropriate explainability levels and techniques by use case and domain. This paper proposes a risk management framework for the implementation of AI in banking with consideration of explainability and outlines the implementation requirements to enable AI to achieve positive outcomes for financial institutions and the customers, markets and societies they serve. The work presents the evaluation of three algorithmic approaches (Neural Networks, Logistic Regression and Type 2 Fuzzy Logic with evolutionary optimisation) for nine banking use cases. We review the emerging regulatory and industry guidance on ethical and safe adoption of AI from key markets worldwide and compare leading AI explainability techniques. We will show that the Type-2 Fuzzy Logic models deliver very good performance which is comparable to or lagging marginally behind the Neural Network models in terms of accuracy, but outperform all models for explainability, thus they are recommended as a suitable machine learning approach for use cases in financial services from an explainability perspective. This research is important for several reasons: (i) there is limited knowledge and understanding of the potential for Type-2 Fuzzy Logic as a highly adaptable, high performing, explainable AI technique; (ii) there is limited cross discipline understanding between financial services and AI expertise and this work aims to bridge that gap; (iii) regulatory thinking is evolving with limited guidance worldwide and this work aims to support that thinking; (iv) it is important that banks retain customer trust and maintain market stability as adoption of AI increases.
Keywords: Regulatory Compliance | Accountability and Explainability | Type-2 Fuzzy Logic | Neural Networks

The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models.
Keywords: Explainable Artificial Intelligence | Interval Type-2 Fuzzy Logic System | Deep Learning

Grid-connected solar-PV schemes have become a significant part of the energy balance in the power system to satisfy the growing request for clean, affordable energy. This study attempts to link solar-PV generation with conventional thermal power plants and to integrate the control zone resulting in a hybrid solar PV-thermal electric power system using an AC tie line. An analysis of the frequency dynamics for varying load conditions of the interconnected system is studied. Diverse approaches of proportional, integral, and proportional-integral fuzzy logic built controllers are design and tested in order to match the electric power with variable loads of the system and hence to normalize the frequency ofthe system in shortest possible time. A comparative analysis of the design topologies is conducted out for the PV-Thermal scheme. Results obtain from the implementation are shown to justify the performance of proposed control efforts, using MATLAB software tool.
Keywords: Solar PV-Thermal electrical power system | frequency dynamics | Proportional| Integral | FLPI control