Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand

This publication has 32 references indexed in Scilit:

• Power load probability density forecasting using Gaussian process quantile regression
  Applied Energy, 2018
• Impact of different time series aggregation methods on optimal energy system design
  Renewable Energy, 2018
• Benchmarking robustness of load forecasting models under data integrity attacks
  International Journal of Forecasting, 2018
• Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
  IEEE Transactions on Smart Grid, 2017
• A better measure of relative prediction accuracy for model selection and model estimation
  Journal of the Operational Research Society, 2015
• A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting
  Energy Conversion and Management, 2015
• Controlling Electricity Consumption by Forecasting its Response to Varying Prices
  IEEE Transactions on Power Systems, 2012
• RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment
  IEEE Transactions on Power Systems, 2008
• A neural network based several-hour-ahead electric load forecasting using similar days approach
  International Journal of Electrical Power & Energy Systems, 2006
• Time Series Forecasting with a Hybrid Clustering Scheme and Pattern Recognition
  IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2004