Development and Validation of a Cooling Load Prediction Model

Abstract

In smart buildings, cooling load prediction is important and essential in the sense of energy efficiency especially in hot countries. Indeed, prediction is required in order to provide the occupant by his consumption and incite him to take right decisions that would potentially decrease his energy demand. In some existing models, prediction is based on a selected reference day. This selection depends on several conditions similarity. Such model needs deep analysis of big past data. Instead of a deep study to well select the reference day; this paper is focusing on a short sampling-rate for predicting the next state. So, this method requires less inputs and less stored data. Prediction results will be more close to the real state. In first phase, an hourly cooling load model is implemented. This model has as input current cooling load, current outside temperature and weather forecast to predict the next hour cooling consumption. To enhance model’s performance and reliability, the sampling period is decreasing to 30 minutes with respect to system dynamic. Lastly, prediction’s accuracy is improved by using previous errors between actual cooling load and prediction results. Simulations are realized in nodes located at a campus showing good adequacy with measurements.

Authors and Affiliations

Abir Khabthani, Leila Châabane

Keywords

Related Articles

Implementing a Safe Travelling Technique to Avoid the Collision of Animals and Vehicles in Saudi Arabia

In this work, a safe travelling technique was proposed and implemented a LoRa based application to avoid the collision of animals with vehicles on the highways of Saudi Arabia. For the last few decades, it has been a gre...

An Automatic Segmentation Algorithm for Solar Filaments in H-Alpha Images using a Context-based Sliding Window

There are many features which appear on the surface of the sun. One of these features that appear clearly are the dark threads in the Hydrogen alpha (Hα) spectrum solar images. These ‘filaments’ are found to have a defin...

Big Data Technology-Enabled Analytical Solution for Quality Assessment of Higher Education Systems

Educational Intelligence is a broad area of big data analytical applications that make use of big data technologies for implementation of solutions for education and research. This paper demonstrates the designing, devel...

Implementation of Machine Learning Model to Predict Heart Failure Disease

In the current era, Heart Failure (HF) is one of the common diseases that can lead to dangerous situation. Every year almost 26 million of patients are affecting with this kind of disease. From the heart consultant and s...

Benefits Management of Cloud Computing Investments

This paper examines investments in cloud computing using the Benefits Management approach. The major contribution of the paper is to provide a unique insight into how organizations derive value from cloud computing inves...

Download PDF file
  • EP ID EP276749
  • DOI 10.14569/IJACSA.2018.090223
  • Views 96
  • Downloads 0

How To Cite

Abir Khabthani, Leila Châabane (2018). Development and Validation of a Cooling Load Prediction Model. International Journal of Advanced Computer Science & Applications, 9(2), 158-164. https://europub.co.uk/articles/-A-276749