Improving energy consumption prediction for residential buildings using Modified Wild Horse Optimization with Deep Learning model.

The research article focuses on a new algorithm termed as Modified Wild Horse Optimization with Deep Learning for Energy Consumption Prediction (MWHODL-ECP), aimed at accurately predicting energy consumption in residential buildings to enhance energy conservation efforts.

Introduction and Objectives

• The paper addresses the significance of energy consumption prediction in residential buildings. Accurate predictions can contribute towards energy conservation and help in making more informed decisions to reduce energy usage.
• The research introduces a novel model coined as Modified Wild Horse Optimization with Deep Learning for Energy Consumption Prediction (MWHODL-ECP), which emphasizes on providing precise forecasts of energy consumed by residential buildings.

Methodology and Implementation

• The processing of data under the MWHODL-ECP model involves several phases such as merging and cleaning data, and normalizing and converting them for modeling.
• The study leverages a Deep Belief Network (DBN), a type of deep learning model, to predict energy consumption. This is known for its ability to identify and learn high-level abstractions and patterns in datasets, making it suitable for complex prediction tasks.
• The Modified Wild Horse Optimization (MWHO) algorithm is used for the fine-tuning of model’s hyperparameters. Hyperparameter tuning is a crucial step in machine learning algorithms, as it influences the overall model’s trainability and performance.

Results and Observations

• The outcomes of the experimental evaluations show that the proposed MWHODL-ECP method outperforms other existing deep learning models in terms of accuracy.
• The MWHODL-ECP model reports improved performance metrics with Mean Squared Error (MSE) of 1.10, Root Mean Squared Error (RMSE) of 1.05, Mean Absolute Error (MAE) of 0.41, R-squared value of 96.28, and a Training time of 1.23.
• The performance metrics suggest that the model has a high degree of reliability in predicting energy consumption, thereby contributing to the enhancement of energy conservation measures in residential buildings.