The United Nations launched sustainable development goals in 2015 that include goals for sustainable energy. From global energy consumption, households consume 20–30% of energy in Europe, North America and Asia; furthermore, the overall global energy consumption has steadily increased in the recent decades. Consequently, to meet the increased energy demand and to promote efficient energy consumption, there is a persistent need to develop applications enhancing utilization of energy in buildings. However, despite the potential significance of AI in this area, few surveys have systematically categorized these applications. Therefore, this paper presents a systematic review of the literature, and then creates a novel taxonomy for applications of smart building energy utilization. The contributions of this paper are (a) a systematic review of applications and machine learning methods for smart building energy utilization, (b) a novel taxonomy for the applications, (c) detailed analysis of these solutions and techniques used for the applications (electric grid, smart building energy management and control, maintenance and security, and personalization), and, finally, (d) a discussion on open issues and developments in the field.

The green energy transition threatens stability of the power grid due to associated reduction in grid synchronous inertia. Primary frequency control (PFC) can compensate for the challenge; however, sufficient procurement of primary frequency capacity could depend on more extensive employment of demand-side loads for PFC. Ventilation fans in particular present a promising class of such loads because of ubiquity of variable-frequency drives and relatively slow thermal and ventilation dynamics of indoor spaces. This research proposes a novel method for PFC by an air handling unit: the open loop control is shown to have favorable dynamic characteristics, and its impact on indoor climate is shown to be tolerable. This study suggests that the largely unused primary frequency capacity of ventilation fans could be exploited to provide primary frequency response for low inertia power grids.

Prediction of faults reliably for Air Handling Units (AHU) is a key aspect of correcting errors and eliminating non-optimal functionality. Machine learning classification methods with data sampling are widely utilized to forecast these kinds of events, which, by their nature, seldom occur in equipment. The model proposed in this paper harnesses seven years of data from an air handling unit that contains information about, for example, temperature, humidity, CO 2 content, and fan speed. This paper contributes to the field of imbalanced classification problems by proposing a novel data undersampling algorithm to enhance the classification model results in the presence of imbalanced and missing data. Moreover, this paper compares several oversampling methods, undersampling methods, probability calibration, and machine learning methods. Then, the paper reports on the proposed final model (proposed undersampling Algorithm 1, Tomek Links, and Logistic Regression) to forecast imperfect heat recovery events in an air handling unit that occur relatively seldom. The precision of the final model was 0.93 for the unseen data; this result was reasonable considering the imbalance of data concurring with missing data sequences.