Energy management is now essential in light of the current energy issues, particularly in the building industry, which accounts for a sizable amount of global energy use. Predicting energy consumption is of great interest in developing an effective energy management strategy. This study aims to prove the outperformance of machine learning models over SARIMA models in predicting heating energy usage in an administrative building in Chefchaouen City, Morocco. It also highlights the effectiveness of SARIMA models in predicting energy with limited data size in the training phase. The prediction is carried out using machine learning (artificial neural networks, bagging trees, boosting trees, and support vector machines) and statistical methods (14 SARIMA models). To build the models, external temperature, internal temperature, solar radiation, and the factor of time are selected as model inputs. Building energy simulation is conducted in the TRNSYS environment to generate a database for the training and validation of the models. The models' performances are compared based on three statistical indicators: normalized root mean square error (nRMSE), mean average error (MAE), and correlation coefficient (R). The results show that all studied models have good accuracy, with a correlation coefficient of 0.90 < R < 0.97. The artificial neural network outperforms all other models (R=0.97, nRMSE=12.60%, MAE= 0.19 kWh). Although machine learning methods, in general terms, seemingly outperform statistical methods, it is worth noting that SARIMA models reached good prediction accuracy without requiring too much data in the training phase. Doi: 10.28991/CEJ-2023-09-05-01 Full Text: PDF

In light of contemporary challenges in the realm of energy, the management of energy has emerged as a critical concern, particularly within the construction sector, an eminent contributor to global energy consumption. With the growing urgency to optimize energy utilization, the ability to forecast energy consumption patterns takes on paramount importance in devising sustainable energy management protocols. Consequently, the present paper seeks to present a comprehensive review concerning the application of artificial neural networks (ANNs) in the prediction of building energy consumption. The subject of the review is explored in depth, with a thorough analysis conducted on how various ANN architectures are commonly used across different situations. Furthermore, the examination expands to include a variety of training algorithms that are employed, shedding light on how each of them contributes to making predictions more accurate. Additionally, the study evaluates the different training functions that are used within ANN models, highlighting their role in improving the accuracy of predictions. By bringing together these different aspects, this paper aims to provide insights that help us understand how ANNs are used to predict building energy usage, showing us how they have evolved, the trends they follow, and the challenges they face. This effort ultimately leads to a better grasp of the important role ANNs play in shaping effective energy management approaches.

The prevalence of extensive glazed areas in contemporary buildings contributes significantly to solar radiation infiltration, elevating energy demands and causing discomfort for occupants. Window shading devices play a pivotal role in addressing this challenge. This paper presents the development and optimization of an artificial neural network (ANN) predictive model, designed to enable real-time control of slat angles by predicting total energy loads, specifically during summer (for cooling and lighting purposes). The refined model demonstrates high precision, achieving a normalized root mean square error (nRMSE) of approximately 1.72% and a correlation coefficient (R) of around 0.999, despite utilizing limited meteorological data. Key inputs for the model include solar radiation, solar altitude, and external temperature, with a particular focus on slat reflectivity. The study assesses the efficiency of three slat types based on their reflectivity: high (80%), medium (50%), and low (20%). Additionally, the research explores the impact of window-to-wall ratio (WWR) values on the control system's efficacy, revealing a positive correlation between higher WWR values and improved energy savings through ANN slat angle control. Furthermore, the study extends the applicability of the ANN model to the six thermal zones in Morocco, affirming its generalization across diverse environmental conditions.

In this paper, the prediction of the internal temperature (Tin) and relative humidity (Rhin) of a greenhouse located near Agadir, Morocco using artificial neural net-work (ANN) as machine learning method. First, an analyze of correlations be-tween inputs and outputs is studied in order to select the adequate input parameters. External temperature, relative humidity and solar radiations were the parameters that have the highest correlation coefficient with the outputs. They are thus selected as the only input parameters. The prediction of Tin and Rhin with the previously cited inputs gives a perfect coefficient of correlation (R=0.996). The aim of this study is to use only one measured input parameter (external temperature) and eliminate the two environmental parameters (relative humidity and solar radiation), by introducing the factor of time as input of the ANN model. Results were very satisfying and 20 neurons was sufficient to reach a correlation of about 0.98.