With the progress of renewable energy generation and energy storage technologies, more and more renewable sources and devices are integrated into the power system. Due to the complexity of the power system, single and multiple power quality disturbances (PQDs) occur more frequently. Hence, real‐time detection of PQDs is the primary issue to mitigate the risk of distortions. This study presents the real‐time PQDs classification using fused convolutional neural networks (CNN) combined with long short‐term memory (fused CNN‐LSTM) architecture based on time and frequency domain features. The frequency‐domain features were obtained from time‐series data using fast Fourier transform. The original time‐domain and frequency‐domain features are extracted by respective CNN‐LSTM structures. The extracted time and frequency domain features are concatenated to classify the PQD through fully connected layers. Our proposed method was trained and tested using 16 types of synthetic noise PQDs data generated by mathematical models, in accordance with the standard IEEE‐1159. Moreover, to further verify the performance of our approach, a simulation distributed power system is carried out to detect various PQDs. We compared three advanced neural network approaches: Deep CNN, CNN‐LSTM, and multifusion CNN (MFCNN). The fused CNN‐LSTM model takes only 0.64 ms to classify each PQDs signal and achieves an accuracy of 98.95% and 98.89% in synthetic data and simulated power systems which indicates our proposed method outperformed compared methods.

Energy consumption in buildings contributes to over a third of global energy consumption and 28% of greenhouse gas emissions. With urbanization and population growth, rising building energy demand can lead to environmental degradation. While significant renewable resources are used to generate electricity to mitigate environmental problems, demand-side management remains crucial for achieving net-zero emissions and enhancing energy efficiency. Accurate building load forecasting is pivotal in devising optimal demand response schemes to shift or reduce the demand on power grids. Recent studies have achieved progressive breakthroughs in building energy forecasting through machine learning algorithms. However, most studies focused on building-level energy forecasting rather than individual load forecasting, which cannot support controlled demand response programs. In this study, we propose a multi-task learning model incorporating Patch, Temporal Convolutional Network and Time-Series Transformer (PatchTCN-TST) based on the channel-independent strategy for floor-level multiple electricity loads and indoor environmental forecasting. The PatchTCN-TST model is implemented to predict future data ranging from one-step ahead to three-step ahead on a real-world office building in Bangkok, Thailand. The experiment results indicate that the prediction performance of our model outperforms the prevalent methods, including LSTM, GRU, TCN, Transformer, Informer and Autoformer. The PatchTCN-TST model demonstrates superior accuracy in three forecasting scenarios, significantly reducing MAE, MSE, RMSE, and aSMAPE by 34%, 23%, 12%, and 36.4%, respectively, compared to the best baseline model.

Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.