Energy consumption forecasting for buildings has immense value in energy efficiency and sustainability research. Accurate energy forecasting models have numerous implications in planning and energy optimization of buildings and campuses. For new buildings, where past recorded data is unavailable, computer simulation methods are used for energy analysis and forecasting future scenarios. However, for existing buildings with historically recorded time series energy data, statistical and machine learning techniques have proved to be more accurate and quick. This study presents a comprehensive review of the existing machine learning techniques for forecasting time series energy consumption. Although the emphasis is given to a single time series data analysis, the review is not just limited to it since energy data is often co-analyzed with other time series variables like outdoor weather and indoor environmental conditions. The nine most popular forecasting techniques that are based on the machine learning platform are analyzed. An in-depth review and analysis of the ‘hybrid model’, that combines two or more forecasting techniques is also presented. The various combinations of the hybrid model are found to be the most effective in time series energy forecasting for building.

A review on time series forecasting techniques for building energy consumption

Multicriteria decision-making (MCDM) refers to making decisions in the presence of multiple and usually conflicting criteria. Fuzzy decision-making is used where vague and incomplete data exist for the solution. Fuzzy multicriteria decision-making is one of the most popular problems handled by the researchers in the literature. In this paper, we survey the latest status of fuzzy multicriteria decision-making methods and classify these methods dividing into two parts: fuzzy multiattribute decision-making (MADM) and fuzzy multiobjective decision-making (MODM). Most of the publications are on fuzzy MADM since there are a plenty of classical multiattribute decision-making methods in the literature. Tabular and graphical illustrations for each method are given.

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Fuzzy Multicriteria Decision-Making: A Literature Review

A survey on river water quality modelling using artificial intelligence models: 2000–2020

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.

A review of fuzzy AHP methods for decision-making with subjective judgements

The extensions of ordinary fuzzy sets such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, and neutrosophic sets, whose membership functions are based on three dimensions, aim at collecting experts’ judgments more informatively and explicitly. In the literature, generalized three-dimensional spherical fuzzy sets have been introduced by Kutlu Gundogdu and Kahraman (J Intell Fuzzy Syst 36(1):337–352, 2019a), including their arithmetic operations, aggregation operators, and defuzzification operations. In this paper, our aim is to extend classical analytic hierarchy process (AHP) to spherical fuzzy AHP (SF-AHP) method and to show its applicability and validity through a renewable energy location selection example and a comparative analysis between neutrosophic AHP and SF-AHP.

A novel spherical fuzzy analytic hierarchy process and its renewable energy application

The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1's, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

Fuzzy analytic hierarchy process with interval type-2 fuzzy sets

Disaster management is extremely important in today’s world, which is defined as the organization and management of resources and responsibilities for dealing with all humanitarian aspects of emergencies, in particular preparedness, response and recovery in order to lessen the impact of disasters. Disaster response is one of the critical stages of disaster management, which necessitates spontaneous decision making when a disaster occurs. Fuzzy inference systems are very suitable for such decision making environments since the inputs and outputs of disaster events cannot be sharply defined. This chapter describes potential applications of fuzzy inference systems in disaster response.

Fuzzy inference systems for disaster response

Discounted cash flows methods are very pop- ular in justifying investments. Uncertainty in investment parameters always exists and is inevitable. The fuzzy set theory is capable to capture this uncertainty through the membership functions of these parameters. However, or- dinary fuzzy sets are criticized for having one single membership value for each certain parameter value. To remove this criticism, type-2 fuzzy sets were proposed. In this paper, we developed interval type-2 fuzzy capital budgeting techniques by using both triangular and trape- zoidal interval type-2 fuzzy sets. The developed techniques are interval type-2 fuzzy net present value analysis, interval type-2 fuzzy future value analysis, and interval type-2 fuzzy equivalent uniform annual value analysis. These techniques are applied to a part of a real life investment project. The results show that the techniques produce consistent outputs, which means that the developed tech- niques can be used interchangeably.

Interval Type-2 Fuzzy Capital Budgeting

Capital budgeting requires dealing with high uncertainty from the unknown characteristics of cash flow, interest rate, and study period forecasts for future periods. Many fuzzy extensions of capital budgeting techniques have been proposed and used in a wide range of applications to deal with uncertainty. In this paper, a new fuzzy extension of the most used capital budgeting techniques is proposed. In this content, first interval-valued Fermatean fuzzy sets (IVFFSs) are defined, and the algebraic and aggregation operations are determined for interval-valued Fermatean fuzzy (IVFF) numbers. The formulations of IVFF net present value, IVFF equivalent uniform annual value, and IVFF benefit-cost ratio (B/C) methods are generated. To validate the proposed methods, proposed formulations are illustrated with a hypothetical example, and the results are compared with classical fuzzy capital budgeting techniques.

Irem Ucal Sari

Papers

Fuzzy analytic hierarchy process with interval type-2 fuzzy sets

Fuzzy inference systems for disaster response

Interval Type-2 Fuzzy Capital Budgeting

Extension of capital budgeting techniques using interval-valued Fermatean fuzzy sets

Fuzzy analytic hierarchy process with type-2 fuzzy sets