Day-Ahead Electric Load Forecasting for the Residential Building with a Small-Size Dataset Based on a Self-Organizing Map and a Stacking Ensemble Learning Method

A high-bias, low-variance introduction to Machine Learning for physicists

(2019) Pankaj Mehta et al.   PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

Robust ensemble learning framework for day-ahead forecasting of household based energy consumption

(2018) Mohammad H. Alobaidi et al.   APPLIED ENERGY

Short-Term Forecasting for Energy Consumption through Stacking Heterogeneous Ensemble Learning Model

(2018) Mergani A. Khairalla et al.   Energies

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

(2018) Federico Divina et al.   Energies

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

(2018) Ping-Huan Kuo et al.   Energies

A review of data-driven approaches for prediction and classification of building energy consumption

(2018) Yixuan Wei et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

A review of data-driven building energy consumption prediction studies

(2018) Kadir Amasyali et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Incentive-based demand response for smart grid with reinforcement learning and deep neural network

(2018) Renzhi Lu et al.   APPLIED ENERGY

Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption

(2017) Muhammad Waseem Ahmad et al.   ENERGY AND BUILDINGS

A review and analysis of regression and machine learning models on commercial building electricity load forecasting

(2017) B. Yildiz et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models

(2017) Zeyu Wang et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]

(2016) Ye Ren et al.   IEEE Computational Intelligence Magazine

Clustering based day-ahead and hour-ahead bus load forecasting models

(2016) Ioannis P. Panapakidis   INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS

Using regression analysis to predict the future energy consumption of a supermarket in the UK

(2014) M.R. Braun et al.   APPLIED ENERGY

Artificial neural networks for short-term load forecasting in microgrids environment

(2014) Luis Hernández et al.   ENERGY

Building energy consumption on-line forecasting using physics based system identification

(2014) Xiwang Li et al.   ENERGY AND BUILDINGS

A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines

(2013) Ervin Ceperic et al.   IEEE TRANSACTIONS ON POWER SYSTEMS

Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study

(2012) M. López et al.   ELECTRIC POWER SYSTEMS RESEARCH

A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework

(2012) Luis Hernández et al.   SENSORS

A computational intelligence scheme for the prediction of the daily peak load

(2011) Jawad Nagi et al.   APPLIED SOFT COMPUTING

Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

(2008) S.Sp. Pappas et al.   ENERGY