Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Advanced control systems engineering for energy and comfort management in a building environment--A review

Given restrictions that comfort conditions in the interior of a building are satisfied, it becomes obvious that the problem of energy conservation is a multidimensional one. Scientists from a variety of fields have been working on this problem for a few decades now; however, essentially it remains an open issue. In the beginning of this article, we define the whole problem in which the topics are: energy, comfort and control. Next, we briefly present the conventional control systems in buildings and their advantages and disadvantage. We will also see how the development of intelligent control systems has improved the efficiency of control systems for the management of indoor environment including user preferences. This paper presents a survey exploring state of the art control systems in buildings. Attention will be focused on the design of agent-based intelligent control systems in building environments. In particular, this paper presents a multi-agent control system (MACS). This advanced control system is simulated using TRNSYS/MATLAB. The simulation results show that the MACS successfully manage the user's preferences for thermal and illuminance comfort, indoor air quality and energy conservation.Controller-agent Ambient intelligence Multi-agent control system Building energy management system Fuzzy PID Optimization techniques

Implementation of artificial intelligence techniques in thermal comfort control for passive solar buildings

Artificial Intelligence techniques are used to control thermal comfort levels in a passive solar building. The controller, as well as the necessary group of rules, are described and analysed. Fuzzy logic is used for the first time in passive building control. © 1992

Building visual comfort control with fuzzy reasoning

Approximate reasoning is, in many cases, a more successful control strategy than a classically designed control scheme. Human reasoning can be reasonably well modelled by fuzzy logic. In this paper, fuzzy logic is used to develop a control scheme for visual comfort in buildings used either for home or office. The fuzzy controller is developed, and a linguistic type of control algorithm is presented. Visual comfort and the relevant processes are described. Mathematical models are used to calculate lighting and glare in the building. © 1992

A fuzzy rule-based approach to achieve visual comfort conditions

In this paper, rule based controller is used to achieve visual comfort in buildings. The objective of this paper is to investigate the fuzzification process and develop a working tuning strategy. The performance of the resultant self-tuning rule base controller is tested against input perturbations and set point changes. The advantages of the proposed system are clarified and compared by the classical control systems. © 1995 Taylor & Francis Group, LLC

Knowledge-based versus classical control for solar-building designs

The present paper compares classical control systems with knowledge-based systems in the control of building designs to achieved comfort conditions. Initially the goal has been the minimization of energy usage. For this target, thermostats and PID controllers have been employed. Adaptive and ad hoc first-generation controller implemented for the improvement of specific problems are described next. The achievement of thermal and visual comfort conditions within living and working space fits the application of fuzzy logic expert systems. The structure of a fuzzy control system is described. This paper also discusses the capabilities of the fuzzy logic expert system in the achievement of optimal resource management in passive-building designs. © 1995

Design of a fuzzy set environment comfort system

This paper presents the design of a fuzzy reasoning expert system for the achievement of thermal and visual comfort in buildings. This system does not demand the precise mathematical model of the building to achieve the control law but uses high-level control variables such as thermal and visual comfort. The powerful interactions of the passive components and of the comfort subjectivity match with the application of the fuzzy control theory entirely. Mathematical models are presented, where the actions of the actuators are applied. The design of the rule base is described and, finally, the system is evaluated by using extensive, worst-case, simulation results. © 1995

Thermal-comfort degradation by a visual comfort fuzzy-reasoning machine under natural ventilation

Fuzzy reasoning is used for visual comfort control in buildings. Natural ventilation is used to save energy and contribute to the achievement of thermal comfort. The present paper investigates the impact of natural ventilation on the thermal-comfort index, assuming the implementation of fuzzy reasoning for visual comfort control. Mathematical models are used to calculate the outdoor climate, the indoor climate and the thermal-comfort index. Finally, a fuzzy-reasoning expert-system, using a linguistic type algorithm, is presented. © 1994

An Intelligent MPPT controller based on direct neural control for partially shaded PV system

The development of an effective maximum power point tracking (MPPT) algorithm is important in order to achieve maximum power operation in a photovoltaic system (PV). In this study, a direct neural control (DNC) scheme is developed. The intelligent MPPT controller consists of a hybrid learning mechanism; an on-line learning rule based on gradient decent method and an off-line learning rule based on Big Bang-Big Crunch (BB-BC) algorithm. The effectiveness of the proposed system is tested under partial shading conditions by applying the cascaded converter topology. The feasibility of the DNC is evaluated by the simulation results and compared to the conventional perturbation and observation (P&O) method. © 2015 Elsevier B.V. All rights reserved