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

Applying the structural equation model rule-based fuzzy system with genetic algorithm for trading in currency market

The present study uses the structural equation model (SEM) to analyze the correlations between various economic indices pertaining to latent variables, such as the New Taiwan Dollar (NTD) value, the United States Dollar (USD) value, and USD index. In addition, a risk factor of volatility of currency returns is considered to develop a risk-controllable fuzzy inference system. The rational and linguistic knowledge-based fuzzy rules are established based on the SEM model and then optimized using the genetic algorithm. The empirical results reveal that the fuzzy logic trading system using the SEM indeed outperforms the buy-and-hold strategy. Moreover, when considering the risk factor of currency volatility, the performance appears significantly better. Remarkably, the trading strategy is apparently affected when the USD value or the volatility of currency returns shifts into either a higher or lower state.Knowledge-based Systems, Fuzzy Sets, Structural Equation Model (SEM), Genetic Algorithm (GA), Currency Volatility

A hierarchical Mamdani-type fuzzy modelling approach with new training data selection and multi-objective optimisation mechanisms: A special application for the prediction of mechanical properties of alloy steels

In this paper, a systematic data-driven fuzzy modelling methodology is proposed, which allows to construct Mamdani fuzzy models considering both accuracy (precision) and transparency (interpretability) of fuzzy systems. The new methodology employs a fast hierarchical clustering algorithm to generate an initial fuzzy model efficiently; a training data selection mechanism is developed to identify appropriate and efficient data as learning samples; a high-performance Particle Swarm Optimisation (PSO) based multi-objective optimisation mechanism is developed to further improve the fuzzy model in terms of both the structure and the parameters; and a new tolerance analysis method is proposed to derive the confidence bands relating to the final elicited models. This proposed modelling approach is evaluated using two benchmark problems and is shown to outperform other modelling approaches. Furthermore, the proposed approach is successfully applied to complex high-dimensional modelling problems for manufacturing of alloy steels, using ‘real’ industrial data. These problems concern the prediction of the mechanical properties of alloy steels by correlating them with the heat treatment process conditions as well as the weight percentages of the chemical compositions

Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.Comment: Accepted at Genetic and Evolutionary Computation Conference 2018 (GECCO '18

A genetic algorithm for the design of a fuzzy controller for active queue management

Active queue management (AQM) policies are those policies of router queue management that allow for the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. This paper proposes the adoption of a fuzzy proportional integral (FPI) controller as an active queue manager for Internet routers. The analytical design of the proposed FPI controller is carried out in analogy with a proportional integral (PI) controller, which recently has been proposed for AQM. A genetic algorithm is proposed for tuning of the FPI controller parameters with respect to optimal disturbance rejection. In the paper the FPI controller design metodology is described and the results of the comparison with random early detection (RED), tail drop, and PI controller are presented

Literature Review of the Recent Trends and Applications in various Fuzzy Rule based systems

Fuzzy rule based systems (FRBSs) is a rule-based system which uses linguistic fuzzy variables as antecedents and consequent to represent human understandable knowledge. They have been applied to various applications and areas throughout the soft computing literature. However, FRBSs suffers from many drawbacks such as uncertainty representation, high number of rules, interpretability loss, high computational time for learning etc. To overcome these issues with FRBSs, there exists many extensions of FRBSs. This paper presents an overview and literature review of recent trends on various types and prominent areas of fuzzy systems (FRBSs) namely genetic fuzzy system (GFS), hierarchical fuzzy system (HFS), neuro fuzzy system (NFS), evolving fuzzy system (eFS), FRBSs for big data, FRBSs for imbalanced data, interpretability in FRBSs and FRBSs which use cluster centroids as fuzzy rules. The review is for years 2010-2021. This paper also highlights important contributions, publication statistics and current trends in the field. The paper also addresses several open research areas which need further attention from the FRBSs research community.Comment: 49 pages, Accepted for publication in ijf

Measurements, Models, Systems and Design

531 s.