New Appliance Detection for Nonintrusive Load Monitoring

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Cost-sensitive weighting and imbalance-reversed bagging for streaming imbalanced and concept drifting in electricity pricing classification

National Natural Science Foundation of China Grants 61572201 and 51707041; Guangzhou Science and Technology Plan Project 201804010245; Fundamental Research Funds for the Central Universities 2017ZD052; Guangdong University of Technology Grant from the Financial and Education Department of Guangdong Province 2016[202]; Education Department of Guangdong Province project number 2016KCXTD022; State Grid Technology Project Grant 5211011600RJ

Multi-view deep forecasting for hourly solar irradiance with error correction

Short-term solar irradiance forecasting is crucial in managing power network operations and solar photovoltaic applications. In this paper, a Multi-view Deep Forecasting method with Error Correction (MvDF_EC) for 1-hour ahead solar forecasting is proposed. MvDF_EC comprises of the Multi-view Deep Forecasting method (MvDF) and a robust Radial Basis Function Neural Network trained via minimizing the Localized Generalization Error for compensating the solar forecasting error of MvDF. MvDF consists of three deep neural networks which learn representations of input data from different views. The three views are 1) the hierarchical local temporal information extracted by the Temporal Convolutional Neural Network (TCN), 2) the key context sequential information captured by the Bi-directional Long Short-Term Memory Neural Network with Temporal Attention (BLSTMattn), and 3) long-term temporal dependencies between local temporal patterns filtered by the Convolutional Gated Recurrent Unit Neural Network (C_GRU). The solar forecasting performance of the proposed MvDF_EC is evaluated with the National Solar Radiation Database. Simulation results show that MvDF_EC yields the most accurate solar prediction compared with the benchmarks including the smart persistence and the state-of-the-art models. The lowest relative Root Mean Square Error values for Maraba and Labelle are 22.08% and 27.40%, respectively in 1-hour ahead solar forecasting.National Natural Science Foundation of China under Grants 61876066 and 61572201; Guangzhou Science and Technology Plan Project 201804010245; Department of Finance and Education of Guangdong Province 2016 [202] Key Discipline Construction Program, China; the Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Group [Project Number 2016KCXTD022]; Brunel University London BRIEF Funding, UK

Deep autoencoder with localized stochastic sensitivity for short-term load forecasting

National Natural Science Foundation of China; Guangdong Province Science and Technology Plan Project; Brunel University London; UK BRIEF Funding; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Grou

A probabilistic solar irradiance interval-valued prediction model with multi-objective optimization of reliability, sharpness and stability

Improved interval-valued prediction models for solar power and irradiance forecasting allow enhanced planning and operation of solar power systems. Highly uncertain atmospheric and environmental factors are major challenges of solar irradiance forecasting. Existing upper and lower bound estimation methods mainly focus on narrowing the prediction intervals and minimizing forecasting errors. However, the sensitivity of the interval-valued prediction model is not considered. Sensitivity is described as the model's output fluctuations due to unseen samples. Models with high sensitivity may not perform well in real-life applications under uncertain environments. This paper presents a novel interval-valued prediction model, P_RSS, by simultaneously optimizing the reliability, sharpness, and stability (RSS) for probabilistic solar irradiance interval-valued prediction. With sensitivity regularization, P_RSS has reduced sensitivity to unseen samples with perturbations from training samples and enhanced robustness. An Extreme learning machine (ELM) model is constructed to directly output prediction intervals (PIs) of solar irradiance via a multi-objective optimization of the RSS. An evaluation framework is proposed to verify the RSS performance. Moreover, a new comprehensive evaluation indicator is proposed to evaluate the PIs. Case studies on three American solar irradiance datasets show that P RSS yields outstanding performance against state-of-the-art methods.10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61876066, 62206020, 61572201, 51907031

Urban Regeneration in a Restructuring Executive-led Polity: A Case Study of Hong Kong

organized by Centre of Asian Studies, the University of Hong Kon

HELP: An LSTM-based approach to hyperparameter exploration in neural network learning

Hyperparameter selection is very important for the success of deep neural network training. Random search of hyperparameters for deep neural networks may take a long time to converge and yield good results because the training of deep neural networks with a huge number of parameters for every selected hyperparameter is very time-consuming. In this work, we propose the Hyperparameter Exploration LSTM-Predictor (HELP) which is an improved random exploring method using a probability-based exploration with an LSTM-based prediction. The HELP has a higher probability to find a better hyperparameter with less time. The HELP uses a series of hyperparameters in a time period as input and predicts the fitness values of these hyperparameters. Then, exploration directions in the hyper-parameter space yielding higher fitness values will have higher probabilities to be explored in the next turn. Experimental results for training both the Generative Adversarial Net and the Convolution Neural Network show that the HELP finds hyperparameters yielding better results and converges faster. (c) 2021 Elsevier B.V. All rights reserved

Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems

none5siopenNg WWY; Hu J; Yeung DS; Yin S; ROLI, FABIONg, Wwy; Hu, J; Yeung, Ds; Yin, S; Roli, Fabi