Future energy planning relies on understanding how much energy is produced and
consumed. In response, this study developed a multihybrid adaptive neuro‐fuzzy inference …

This study compares the performance of standalone adaptive neuro-fuzzy inference system
(ANFIS) and its hybrid with particle swarm optimisation (PSO) in predicting the energy …

Energy forecasting is crucial for efficient energy management and planning for future energy
needs. Previous studies have employed hybrid modeling techniques, but insufficient …

Predicting electricity consumption is notably essential to provide a better management
decision and company strategy. This study presents a hybrid machine learning model by …

Accurately forecasting energy consumption is beneficial and pivotal for effectively managing
variable refrigerant flow (VRF) systems. Changes in energy consumption provide an intuitive …

The building sector is the largest energy consumer accounting for 40% of global energy
usage. An energy forecast model supports decision-makers to manage electric utility …

Energy is one of the most critical inputs in social and economic development, is an essential
factor in increasing living standards and creating sustainable development. Since energy is …

Increasing consumption of energy calls for proper approximation of demand towards a
sustainable and cost-effective development. In this work, novel hybrid methodologies aim to …

In this paper, new statistical features based approach (SFBA) for hourly energy consumption
prediction using Multi-Layer Perceptron is presented. The model consists of four stages …

Building energy data analysis is a major branch of smart city development research. The
usual back propagation neural network model for building energy prediction has problems …