Digital Cities Roadmap. Группа авторов

Digital Cities Roadmap

Hidden Markov Flexible generalization of sequence profiles; can handle Requires training using annotated data: Many unstructured parameters Daily living activities recognition classification Deep Learning Enables learning of feature rather than hand tuning: Reduce the need for feature engineering Requires a very large amount of labeled data. computationally really expensive, and extremely hard to tune Modeling occupied a behavior, and in human voice recognition and monitoring systems; Context-aware SB services Regression Orthogonal matching pursuit Fast Can go seriously wrong if there are severe outliers or influential cases For regression problem such as energy efficiency services in SBs clustered based Straightforward to understand and explain, and can be regularized to avoid overfitting It is not flexible enough to capture complex patterns Gesture recognition Ensemble methods N/A Increased model accuracy through averaging as the number of model increases Difficulties in interpreting decisions; Large computational requirements Human activity recognition and energy efficiency services Time Series N/A Can model temporal relationships; Applicable to settings where traditional between subject design are impossible or difficult to implement Model identification is difficult; Traditional measures may be inappropriate for TS designs; Generalizability cannot be inferred from a single study Occupant comfort services and energy efficiency services in SBs Unsupervised learning Clustering KNN Simplicity: Easy to implement and interpret; Fast and computationally efficient High computation cost; lazy learner Human activity recognition. K-pattern clustering Simple; Easy to implement and interpret; Fast and computationally efficient Only locally optimal and sensitive to initial points; Difficult to predict K-Value Predict user activities in smart environments Others N/A N/A N/A Semi- Supervised Learning N/A N/A Overcome the problem of supervised learning having not enough labeled data False labeling problems and incapable of utilizing out-ofdomain samples Provide context aware services such as health monitoring and elderly care services Reinforcement learning N/A N/A Used “deeper” knowledge about domain Must have a model of environment; must know where actions lead in order to evaluate actions Lighting control services and learning the occupants, preferences of music and lighting services.

Ensemble Methods: The community of classification models that are educated separately and then predictions are merged in a way that generates the ultimate prevision, also referred to as classifier ensemble.

Unsupervised learning implies designing algorithms to use data that have no labeling to evaluate the behavior or structure being analyzed. The algorithm is the best techniques to work on its own to discover patterns and information that was previously undetected. Clustering, anomaly, detection, Neural Networks etc. are all the examples of unsupervised learning.

Clustering: The internal groupings in the products, such as the grouping of customers, are investigated through a clustering problem. Modeling approaches including centroid-based and hierarchical are typically organized through clustering techniques.

Association: The question of the association rule is used to classify laws that describe significant quantities of input data, such as individuals who purchase X products, who also purchase Y objects. Association research can be achieved by evaluating rules for repeated if/then statement inputs and utilizing help requirements and trust to distinguish associations between unconnected data in a relational database.

Semi-Supervised Learning: Semi-controlled instruction is between approaches regulated and unregulated. Information is a labeled and blank experimental combination. Such architectures are synthetic are intended to consider and counteract the weaknesses of the main groups.

Reinforcement Learning: To order to optimize the principle of accrual compensation, enhanced learning, an ML area influenced by behavioral science, is concerned with the way virtual agents are to work to an environment. RL algorithms are used to learn policy of control, particularly if no prior information exists and a large amount of training data are available.

1.10.5 Machine Learning Tasks in Smart Building Environment

The key ML activities that are applicable to SB will be identified. For the general description of ML activities in SBs and measures to incorporate ML in an SB setting the reader is alluded to in Figure 1.24.

Collecting and collecting data: A range of methods were used to collect data, each of varying resources, energy consumption and networking deals. Sensors and related artifacts in SBs simultaneously produce raw information and these devices can store or record the information on monitored components for a specified period of time.

Schematic illustration of ML tasks in SB Environment.

Figure 1.24 ML tasks in SB Environment.

It can be utilized by decision-makers, planners, running and sustaining staff and building customers, many healthcare services and so on.

Data Pre-processing: Much data is generated in SBs by sensors from various sources with specific formats and architectures. The data come from different sources. This knowledge is not usually ready to be evaluated, since its poor battery capacity, bad tuning, access to numerous harmful elements and intervention may be incomplete or redundant.

Dimensionality Reduction: Raising volumes of raw data from heterogenous and all-embracing sensors used in SBs are enormous. The bulk of data from these sensors is redundant and needs to be minimized by utilizing techniques to limit their dimensionality to a smaller number of features without missing any valuable details.

1.10.6 ML Tools and Services for Smart Building

The

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