Energy performance forecasting of residential buildings using fuzzy approaches

The energy consumption used for domestic purposes in Europe is, to a considerable extent, due to heating and cooling. This energy is produced mostly by burning fossil fuels, which has a high negative environmental impact. The characteristics of a building are an important factor to determine the necessities of heating and cooling loads. Therefore, the study of the relevant characteristics of the buildings, regarding the heating and cooling needed to maintain comfortable indoor air conditions, could be very useful in order to design and construct energy-efficient buildings. In previous studies, different machine-learning approaches have been used to predict heating and cooling loads from the set of variables: relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution. However, none of these methods are based on fuzzy logic. In this research, we study two fuzzy logic approaches, i.e., fuzzy inductive reasoning (FIR) and adaptive neuro fuzzy inference system (ANFIS), to deal with the same problem. Fuzzy approaches obtain very good results, outperforming all the methods described in previous studies except one. In this work, we also study the feature selection process of FIR methodology as a pre-processing tool to select the more relevant variables before the use of any predictive modelling methodology. It is proven that FIR feature selection provides interesting insights into the main building variables causally related to heating and cooling loads. This allows better decision making and design strategies, since accurate cooling and heating load estimations and correct identification of parameters that affect building energy demands are of high importance to optimize building designs and equipment specifications.Peer ReviewedPostprint (published version

Biochar and Application of Machine Learning: A Review

This study discusses biochar and machine learning application. Concept of biochar, machine learning and different machine learning algorithms used for predicting adsorption onto biochar were examined. Pyrolysis is used to produce biochar from organic materials. Agricultural wastes are burnt in regulated conditions to produce charcoal-like biochar using pyrolysis. Biochar plays a major role in removing heavy metals. Biochar is eco-friendly, inexpensive and effective. Increasing interest in biochar is due to stable carbon skeleton because of ease of sourcing the precursor feedstock and peculiar physicochemical. However, artificial intelligence is a process of training computers to mimic and perform duties human. Artificial intelligence aims to enable computers to solve human challenges and task like humans. A branch of artificial intelligence that teaches machine to perform and predict task using previous data is known as machine learning. It uses parameters called algorithms that convert previous data (input) to forecast new solution. Algorithms that have been used in biochar applications are examined. It was discovered that neural networks, eXtreme Gradient Boosting algorithm and random forest for constructing and evaluating the predictive models of adsorption onto biochar have all been used for biochar application. Machine learning prevents waste, reduces time and reduces cost. It also permits an interdisciplinary means of removing heavy metals

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Fuzzy machine vision based inspection

Machine vision system has been fostered to solve many realistic problems in various fields. Its role in achieving superior quality and productivity is of paramount importance. But, for such system to be attractive, it needs to be fast, accurate and cost-effective. This dissertation is based on a number of practical machine vision based inspection projects obtained from the automotive industry. It presents a collection of developed efficient fuzzy machine vision approaches endorsed with experimental results. It also covers the conceptual design, development and testing of various fuzzy machine vision based inspection approaches for different industrial applications. To assist in developing and evaluating the performance of the proposed approaches, several parts are tested under varying lighting conditions. This research deals with two important aspects of machine vision based inspection. In the first part, it concentrates on the topics of component detection and component orientation identification. The components used in this part are metal clips mounted on a dash panel frame that is installed in the door of trucks. Therefore, we propose a fuzzy machine vision based clip detection model and a fuzzy machine vision based clip orientation identification model to inspect the proper placement of clips on dash panels. Both models are efficient and fast in terms of accuracy and processing time. In the second part of the research, we are dealing with machined part defects such as broken edge, porosity and tool marks. The se defects occur on the surface of die cast aluminum automotive pump housings. As a result, an automated fuzzy machine vision based broken edge detection method, an efficient fuzzy machine vision based porosity detection technique and a neuro-fuzzy part classification model based on tool marks are developed. Computational results show that the proposed approaches are effective in yielding satisfactory results to the tested image databases. There are four main contributions to this work. The first contribution is the development of the concept of composite matrices in conjunction with XOR feature extractor using fuzzy subtractive clustering for clip detection. The second contribution is about a proposed model based on grouping and counting pixels in pre-selective areas which tracks pixel colors in separated RGB channels to determine whether the orientation of the clip is acceptable or not. The construction of three novel edge based features embedded in fuzzy C-means clustering for broken edge detection marks the third contribution. At last, the fourth contribution presents the core of porosity candidates concept and its correlation with twelve developed matrices. This, in turn, results in the development of five different features used in our fuzzy machine vision based porosity detection approach

Context dependent fuzzy modelling and its applications

Fuzzy rule-based systems (FRBS) use the principle of fuzzy sets and fuzzy logic to describe vague and imprecise statements and provide a facility to express the behaviours of the system with a human-understandable language. Fuzzy information, once defined by a fuzzy system, is fixed regardless of the circumstances and therefore makes it very difficult to capture the effect of context on the meaning of the fuzzy terms. While efforts have been made to integrate contextual information into the representation of fuzzy sets, it remains the case that often the context model is very restrictive and/or problem specific. The work reported in this thesis is our attempt to create a practical frame work to integrate contextual information into the representation of fuzzy sets so as to improve the interpretability as well as the accuracy of the fuzzy system. Throughout this thesis, we have looked at the capability of the proposed context dependent fuzzy sets as a stand alone as well as in combination with other methods in various application scenarios ranging from time series forecasting to complicated car racing control systems. In all of the applications, the highly competitive performance nature of our approach has proven its effectiveness and efficiency compared with existing techniques in the literature

Fuzzy Decision Making and Soft Computing Applications

This Special Issue collects original research articles discussing cutting-edge work as well as perspectives on future directions in the whole range of theoretical and practical aspects in these research areas: i) Theory of fuzzy systems and soft computing; ii) Learning procedures; iii) Decision-making applications employing fuzzy logic and soft computing

Fuzzy Logic

The capability of Fuzzy Logic in the development of emerging technologies is introduced in this book. The book consists of sixteen chapters showing various applications in the field of Bioinformatics, Health, Security, Communications, Transportations, Financial Management, Energy and Environment Systems. This book is a major reference source for all those concerned with applied intelligent systems. The intended readers are researchers, engineers, medical practitioners, and graduate students interested in fuzzy logic systems