A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.

Nonintrusive Monitoring for Electric Vehicles Based on Zero-Shot Learning

Monitoring the charging behavior of electric vehicle clusters will contribute to developing more effective energy management strategies for grid operators. A low implementation cost leads to a wide application prospect in nonintrusive monitoring for EVs. Aiming at the problem that traditional nonintrusive monitoring methods cannot identify unknown devices accurately due to the lack of classes, a nonintrusive monitoring method based on zero-shot learning (ZSL) is proposed in this article, one which can monitor the unknown types of EVs connected to charging piles. First, the charging characteristics of known EVs and unknown EVs are extracted by dictionary learning. Then EVs are classified by ZSL based on sparse coding. Furthermore, EVs are decomposed based on the proposed multimode factorial hidden Markov model (FHMM). Finally, the EV dataset of Pecan Street is used to verify the effectiveness and accuracy of the proposed method.

Internet of Things and Machine Learning for Healthy Ageing: Identifying the Early Signs of Dementia

Identifying the symptoms of the early stages of dementia is a difficult task, particularly for older adults living in residential care. Internet of Things (IoT) and smart environments can assist with the early detection of dementia, by nonintrusive monitoring of the daily activities of the older adults. In this work, we focus on the daily life activities of adults in a smart home setting to discover their potential cognitive anomalies using a public dataset. After analysing the dataset, extracting the features, and selecting distinctive features based on dynamic ranking, a classification model is built. We compare and contrast several machine learning approaches for developing a reliable and efficient model to identify the cognitive status of monitored adults. Using our predictive model and our approach of distinctive feature selection, we have achieved 90.74% accuracy in detecting the onset of dementia.

Study of Fine-Grained Cementitious Composites in Solidification Phase Using Acoustic Testing Technique

The paper deals with experimental analysis, which is focused on the use of acoustic measurement during the solidification process. As a material for monitoring was chosen fine-grained cementitious composites in the laboratory environment. For this purpose, a measuring device working on the principle of mechanical waves passing through the material was designed, assembled and verified. The experiment was conducted on cement pastes prepared from CEM I 42.5 R Portland cement with two different water coefficients (w/c = 0.40 and w/c = 0.33). The differences in the wave propagation in cement pastes were investigated. Simultaneously with this experiment, the monitoring and the saving records of the internal temperature was conducted. The results show the time of „critical changes" in the internal structure of the material can be determined. These changes are probably related to the quality of the particle’s bonds in the inner material structure, which is reflected in the propagation of mechanical waves. Overall, it is shown these experiments could be used to expand the understanding of the various processes occurring during early hydration of cement, and the application of these results to field situations (in the future) could lead to the other development of, non-destructive (and nonintrusive) monitoring techniques.