Load forecasting based on deep neural network and historical data augmentation

A Methodological Framework to support Load Forecast Error Assessment in Local Energy Markets

(2020) Sebastian Schreck et al.   IEEE Transactions on Smart Grid

Two-Layer Transfer-Learning-Based Architecture for Short-Term Load Forecasting

(2019) Long Cai et al.   IEEE Transactions on Industrial Informatics

Power Market Load Forecasting on Neural Network with Beneficial Correlated Regularization

(2018) Fang Yuan Xu et al.   IEEE Transactions on Industrial Informatics

Robust Power System Security Assessment Under Uncertainties Using Bi-Level Optimization

(2018) Naoto Yorino et al.   IEEE TRANSACTIONS ON POWER SYSTEMS

Short-term Load Forecasting with Deep Residual Networks

(2018) Kunjin Chen et al.   IEEE Transactions on Smart Grid

A novel hybrid prediction model for aggregated loads of buildings by considering the electric vehicles

(2018) Min Duan et al.   Sustainable Cities and Society

A robust correlation analysis framework for imbalanced and dichotomous data with uncertainty

(2018) Chun Sing Lai et al.   INFORMATION SCIENCES

Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques

(2018) Mengmeng Cai et al.   APPLIED ENERGY

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

(2017) Justin Salamon et al.   IEEE SIGNAL PROCESSING LETTERS

An Accurate and Fast Converging Short-Term Load Forecasting Model for Industrial Applications in a Smart Grid

(2017) Ashfaq Ahmad et al.   IEEE Transactions on Industrial Informatics

A New Feature Selection Technique for Load and Price Forecast of Electrical Power Systems

(2017) Oveis Abedinia et al.   IEEE TRANSACTIONS ON POWER SYSTEMS

Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network

(2017) Weicong Kong et al.   IEEE Transactions on Smart Grid

Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

(2016) Tingting Fang et al.   APPLIED ENERGY

An ensemble approach for short-term load forecasting by extreme learning machine

(2016) Song Li et al.   APPLIED ENERGY

Using the ensemble Kalman filter for electricity load forecasting and analysis

(2016) Hisashi Takeda et al.   ENERGY

Deep belief network based electricity load forecasting: An analysis of Macedonian case

(2016) Aleksandra Dedinec et al.   ENERGY

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

(2016) Sasan Barak et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression

(2016) Guo-Feng Fan et al.   NEUROCOMPUTING

A hybrid self-adaptive Particle Swarm Optimization–Genetic Algorithm–Radial Basis Function model for annual electricity demand prediction

(2015) Shiwei Yu et al.   ENERGY CONVERSION AND MANAGEMENT

Day-ahead load forecast using random forest and expert input selection

(2015) A. Lahouar et al.   ENERGY CONVERSION AND MANAGEMENT

Models and Techniques for Electric Load Forecasting in the Presence of Demand Response

(2015) Andrea Garulli et al.   IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY

Deep learning

(2015) Yann LeCun et al.   NATURE

Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm

(2015) Oveis Abedinia et al.   International Transactions on Electrical Energy Systems

A new short-term load forecast method based on neuro-evolutionary algorithm and chaotic feature selection

(2014) Sajjad Kouhi et al.   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Applying the ensemble artificial neural network-based hybrid data-driven model to daily total load forecasting

(2014) Jia-rui Dong et al.   NEURAL COMPUTING & APPLICATIONS

Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine

(2013) Rui Zhang et al.   IET Generation Transmission & Distribution