M. Pasadas

River flow time series using least squares support vector machines

LSTM network: a deep learning approach for short‐term traffic forecast

A novel double incremental learning algorithm for time series prediction

A review on applications of ANN and SVM for building electrical energy consumption forecasting

Characterisation and technical feasibility of using biomass bottom ash for civil infrastructures

Comparative analysis of image classification algorithms based on traditional machine learning and deep learning

A CNN–LSTM model for gold price time-series forecasting

A methodology for Electric Power Load Forecasting

Ash-related issues during biomass combustion: Alkali-induced slagging, silicate melt-induced slagging (ash fusion), agglomeration, corrosion, ash utilization, and related countermeasures

A review on time series forecasting techniques for building energy consumption

Green building research–current status and future agenda: A review

Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm

Forecasting the short-term metro passenger flow with empirical mode decomposition and neural networks

Renewable energy in buildings in China—A review

A review on energy scenario and sustainable energy in Indonesia

Potential use of biomass bottom ash as alternative construction material: Conflictive chemical parameters according to technical regulations

A global review of energy consumption, CO 2 emissions and policy in the residential sector (with an overview of the top ten CO 2 emitting countries)

Hybridization of intelligent techniques and ARIMA models for time series prediction

Soft-computing techniques and ARMA model for time series prediction

Biomass potential in Andalusia, from grapevines, olives, fruit trees and poplar, for providing heating in homes