In this article, the consumption of energy in Internet-of-things-based smart buildings is investigated. The main goal of this work is to predict cooling and heating loads as the parameters that impact the amount of energy consumption in smart buildings, some of which have the property of symmetry. For this purpose, it proposes novel machine learning models that were built by using the tri-layered neural network (TNN) and maximum relevance minimum redundancy (MRMR) algorithms. Each feature related to buildings was investigated in terms of skewness to determine whether their distributions are symmetric or asymmetric. The best features were determined as the essential parameters for energy consumption. The results of this study show that the properties of relative compactness and glazing area have the most impact on energy consumption in the buildings, while orientation and glazing area distribution are less correlated with the output variables. In addition, the best mean absolute error (MAE) was calculated as 0.28993 for heating load (kWh/m2) prediction and 0.53527 for cooling load (kWh/m2) prediction, respectively. The experimental results showed that our method outperformed the state-of-the-art methods on the same dataset.

Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a timely manner. However, a standard ML algorithm cannot be directly applied to a PdM dataset, which is highly imbalanced since, in most cases, signals correspond to normal rather than critical conditions. To deal with data imbalance, in this paper, a novel explainable ML method entitled “Balanced K-Star” based on the K-Star classification algorithm is proposed for PdM in an IoT-based manufacturing environment. Experiments conducted on a PdM dataset showed that the proposed Balanced K-Star method outperformed the standard K-Star method in terms of classification accuracy. The results also showed that the proposed method (98.75%) achieved higher accuracy than the state-of-the-art methods (91.74%) on the same data.

Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.