Prediction of Energy Consumption of an Administrative Building using Machine Learning and Statistical Methods

oai:ojs.pkp.sfu.ca:article/4099Energy 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: PD

Influence of gravel and adjuvant on the compressive strength and water absorption of concrete

Concrete is the most commonly used material in civil engineering, given its economic cost and ease of manufacture. Its strength depends on the characteristics of its constituents. A good mix makes it possible to build solid, durable and economical structures. The present work aims to characterize the gravel of the Eastern region (quarry of eastern Morocco) by granulometric analysis and water absorption. Then, the studied gravel is used to produce three types of concrete (B20, B25 and B30), which were assessed in terms of water absorption and compressive strength. The last step is to study the effect of an adjuvant, more specifically a water reducer, on mechanical characteristics of local concrete. B25 concrete was chosen for the last step since it is the most used type in the region. Results show that adding a water reducer adjuvant, in this case ‘Chrysoplast’, can improve the compressive strength of concrete if the percentage added is accurately determined

The prediction of the inside temperature and relative humidity of a greenhouse using ANN method with limited environmental and meteorological data

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