In this paper, we focus on data-driven approaches to human activity recognition (HAR). Data-driven approaches rely on good quality data during training, however, a shortage of high quality, large-scale, and accurately annotated HAR datasets exists for recognizing activities of daily living (ADLs) within smart environments. The contributions of this paper involve improving the quality of an openly available HAR dataset for the purpose of data-driven HAR and proposing a new ensemble of neural networks as a data-driven HAR classifier. Specifically, we propose a homogeneous ensemble neural network approach for the purpose of recognizing activities of daily living within a smart home setting. Four base models were generated and integrated using a support function fusion method which involved computing an output decision score for each base classifier. The contribution of this work also involved exploring several approaches to resolving conflicts between the base models. Experimental results demonstrated that distributing data at a class level greatly reduces the number of conflicts that occur between the base models, leading to an increased performance prior to the application of conflict resolution techniques. Overall, the best HAR performance of 80.39% was achieved through distributing data at a class level in conjunction with a conflict resolution approach, which involved calculating the difference between the highest and second highest predictions per conflicting model and awarding the final decision to the model with the highest differential value.

Current methods for non-intrusive load monitoring problems assume that the number of appliances in the target location is known, however, this may not be realistic. In real-world situations, the initial setup of the site can be known but new appliances may be added by users after a period of time, especially in a household or non-restrictive scenarios. In this sense, current methods without detecting new appliances may not accurately monitor loads of different appliances and scenarios. In this work, a novel new appliance detection method is proposed for non-intrusive load monitoring with imbalance classification for appliances switching on or off. The prediction of an appliances being switched on or off is an important step in load monitoring and the switching on frequencies for coffee machine and air conditioning in a household are different, making the problem inherently imbalanced. Experimental results show that the proposed method yields outstanding performance against the well-known oversampling method, Synthetic Minority Oversampling Technique (SMOTE), on real non-intrusive load monitoring applications in scenarios with new appliances emerging.

Undersampling is a popular method to solve imbalanced classification problems. However, sometimes it may remove too many majority samples which may lead to loss of informative samples. In this article, the hashing-based undersampling ensemble (HUE) is proposed to deal with this problem by constructing diversified training subspaces for undersampling. Samples in the majority class are divided into many subspaces by a hashing method. Each subspace corresponds to a training subset which consists of most of the samples from this subspace and a few samples from surrounding subspaces. These training subsets are used to train an ensemble of classification and regression tree classifiers with all minority class samples. The proposed method is tested on 25 UCI datasets against state-of-the-art methods. Experimental results show that the HUE outperforms other methods and yields good results on highly imbalanced datasets.

As efficient recurrent neural network (RNN) models, echo state networks (ESNs) have attracted widespread attention and been applied in many application domains in the last decade. Although they have achieved great success in modeling time series, a single ESN may have difficulty in capturing the multitimescale structures that naturally exist in temporal data. In this paper, we propose the convolutional multitimescale ESN (ConvMESN), which is a novel training-efficient model for capturing multitimescale structures and multiscale temporal dependencies of temporal data. In particular, a multitimescale memory encoder is constructed with a multireservoir structure, in which different reservoirs have recurrent connections with different skip lengths (or time spans). By collecting all past echo states in each reservoir, this multireservoir structure encodes the history of a time series as nonlinear multitimescale echo state representations (MESRs). Our visualization analysis verifies that the MESRs provide better discriminative features for time series. Finally, multiscale temporal dependencies of MESRs are learned by a convolutional layer. By leveraging the multitimescale reservoirs followed by a convolutional learner, the ConvMESN has not only efficient memory encoding ability for temporal data with multitimescale structures but also strong learning ability for complex temporal dependencies. Furthermore, the training-free reservoirs and the single convolutional layer provide high-computational efficiency for the ConvMESN to model complex temporal data. Extensive experiments on 18 multivariate time series (MTS) benchmark datasets and 3 skeleton-based action recognition datasets demonstrate that the ConvMESN captures multitimescale dynamics and outperforms existing methods.

Accurate load forecasting is essential to the operation and planning of power systems and electricity markets. In this paper, an ensemble of radial basis function neural networks (RBFNNs) is proposed which is trained by minimizing the localized generalization error for short-term and mid-term load forecasting. Exogenous features and features extracted from load series (with long short-term memory networks and multi-resolution wavelet transform) in various timescales are used to train the ensemble of RBFNNs. Multiple RBFNNs are fused as an ensemble model with high generalization capability using a proposed weighted fusion method based on the localized generalization error model. Experimental results on three practical datasets show that compared with other forecasting methods, the proposed method reduces the mean absolute percentage error (MAPE), mean squared error (MSE), mean absolute error (MAE) by at least 0.12%, 8.46 (MW)2, 0.83 MW in mid-term load forecasting (i.e., to predict the daily peak load of next month), respectively, and reduces the MAPE, MSE by at least 0.19%, 2009.69 (MW)2 and 0.30%, 3697.18 (MW)2 in half-hour-ahead forecasting and day-ahead forecasting, respectively.

In data streaming environments such as smart grid, it is impossible to restrict arriving data to have the same number of samples in each class. Hence, in addition to concept drift, classification problems in streaming data environments are inherently imbalanced. However, these problems in smart grid have rarely been studied. Incremental learning aims to learn correct classification for future unseen samples from the given streaming data. In this work, we propose a new incremental ensemble learning method to handle both issues. The class imbalance issue is tackled by an imbalance-reversed bagging method while the adaptation to concept drift is achieved by a dynamic cost-sensitive weighting scheme for component classifiers. The proposed methodology is applied to a case study for the electricity pricing in New South Wales and Victoria, Australia. Experimental results show the effectiveness of the proposed algorithm with statistical significance in comparison to the state-of-the-art incremental learning methods.