Energies 2023, 16, 22. https://doi.org/10.3390/en16010022 www.mdpi.com/journal/energies Editorial Machine Intelligence in Smart Buildings Anastasios I. Dounis Department of Biomedical Engineering, University of West Attica, 12243 Egaleo, Greece; aidounis@uniwa.gr Energy efficiency is a key concern in achieving sustainability in modern society. Smart city sustainability depends on the availability of energy-efficient infrastructures and services. Buildings comprise most of the city, and they are responsible for more than two-thirds of global CO2 emissions and energy use. The rapid evolution of artificial intelligence (AI) and machine learning (ML) has equipped buildings with an ability to learn. Machine intelligence (MI) has been used to learn patterns of living and behaviors, to thus regulate the use of energies. MI represents a collection or a set of various technologies such as the Internet of Things (IoT), deep learn- ing (DL), deep reinforcement learning (DR), fuzzy logic systems (FLS), digital twins (DW), artificial intelligence (AI), machine learning (ML), and evolutionary algorithms (EA) in- volving non-linear dynamics, computational intelligence, ideas drawn from physics, physiology, and several other computational frameworks. MI investigates, simulates, and analyzes very complex issues and phenomena in order to solve real-world problems re- quiring a multidisciplinary approach. MI can be considered as a higher evolution of ML, and a stepping-stone on the roadway to explainable AI (XAI). This Editorial on “Machine Intelligence in Smart Buildings” includes a collection of ten excellent papers covering different technological aspects of advanced machine intelli- gence in smart buildings, ranging from intelligent energy predictions based on deep neu- ral networks [1] to occupant predictions based on IoT technologies [2]; from machine learning classifiers for energy efficiency classifications of buildings [3] to building energy management based on digital twin and artificial intelligence approach [4]; from an energy and comfort management optimization approach using evolutionary algorithms [5] to ex- ploring the potentialities of deep reinforcement learning for incentive-based demand re- sponse in a cluster of small commercial buildings [6]; from fuzzy control systems for smart energy management in buildings [7] to a systematic review contribution and risk of AI in building smart cities [8]; and finally, unsupervised machine learning for smart building energy inefficiencies through time series [9] and smart electrochromic windows to en- hance building energy efficiency and visual comfort [10]. A summary of the content asso- ciated with each of the selected papers presented in this Editorial is presented subse- quently. Sadeghi, Sinaki et al. used deep neural networks (DNNs) to forecast heating and cooling loads (HL and CL, respectively) to measure the energy performance of buildings (EPB). The DNNs explored in their study include multi-layer perceptron (MLP) networks, and each of the models studied was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN was shown to be an improvement for modeling HLs and CLs compared with tra- ditional artificial neural network (ANN) models. To extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods— although both have the same purpose in terms of determining the significance of inde- pendent variables within a model. Local SA assumes that inputs are independent of each other, whereas global SA does not. To further develop the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity Citation: Dounis, A.I. Machine Intelligence in Smart Buildings. Energies 2023, 16, 22. https://doi.org/ 10.3390/en16010022 Received: 29 October 2022 Accepted: 27 November 2022 Published: 20 December 2022 Copyright: © 2022 by the author. Li- censee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and con- ditions of the Creative Commons At- tribution (CC BY) license (https://cre- ativecommons.org/licenses/by/4.0/).