Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load

Mutombo, Ntumba Marc-Alain; Numbi, B.P.

doi:10.3390/en15145097

Cited by 10 publications

(5 citation statements)

References 30 publications

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“…Fig. 1 LR Flow chart [17] Random Forest: Assume you have a forest with several trees, each with its own unique method of forecasting things.…”

Section: B Machine Learningmentioning

confidence: 99%

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

Le,

Dong

2024

COMIEN

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The biogas has the potential to serve as a substitute as fossil derived fuel. The present study investigates anaerobic co-digestion of organic waste for predictive modelling. Several co-digestion studies were performed with different pH, solid concentration, temperature, and co-digestion ratios. A water displacement apparatus was used to test biogas yield, and data was collected meticulously. Linear regression (LR) and Random Forest (RF) based models were built with Python-based tools and tested using statistical measures for the prediction of biogas yield. The LR showed a strong linear association, with R and R2 values of 0.9892 and 0.9785, respectively. However, RF surpassed LR, with higher R and R2 values of 0.9919 and 0.9826, respectively. Furthermore, RF had lower MSE and MAE values, indicating higher prediction accuracy and precision. RF consistently scored well in tests, demonstrating its ability to capture complicated relationships while minimizing prediction mistakes. Taylor's diagrams further demonstrated RF's excellent performance during both the training and testing periods. Overall, RF emerges as the optimum model for reliably estimating biogas output in anaerobic co-digestion systems, with important implications for waste-to-energy processes.

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“…Fig. 1 LR Flow chart [17] Random Forest: Assume you have a forest with several trees, each with its own unique method of forecasting things.…”

Section: B Machine Learningmentioning

confidence: 99%

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

Le,

Dong

2024

COMIEN

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“…The results showed that the model performed well in predicting occupancy and showed satisfactory results. Similarly, in the article in [ 124 ], the authors described the development of a linear multi-regression model to predict the cooling load of a room in the Renewable Energy Research Laboratory at Mangosuthu University of Technology, using radiant time series method components. The model considered several predictors, including male and female occupants, window cooling load, and roof cooling load, which were identified as the most influential factors in determining the cooling load of the room.…”

Section: Data Analysis Approachmentioning

confidence: 99%

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Khan,

Zedadra,

Guerrieri

et al. 2024

Sensors

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In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

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“…SWHs (FPC, ETC) sustainability life cycle environment in regions with low solar irradiation, such as the United Kingdom, was developed by simulation. The main conclusion was that solar thermal systems do not represent a more environmentally sustainable alternative to fossil fuel-based water heating (Mutombo & Numbi, 2022). Dust effect on the performance of the solar collector was analyzed in Hakizabera, Li, Yang, and Heli (2018).…”

Section: International Journal Of Sustainable Energy and Environmenta...mentioning

confidence: 99%

Weather influence on the performance of solar collector

Olivier¹,

Emmanuel

Gloria

2023

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A laboratory-scale evacuated tube solar collector (ETSCs) has been developed and tested for unloading conditions in Lanzhou, China. clear sky and cloudy conditions have been tested. Heat is generally considered instantaneous because it is a function of multiple instantaneous factors, like ambient temperature and solar radiation. The results show a clear sky day with a maximum and average value of ambient temperature, outlet, inlet, and solar radiation of 12 and -60C, 56 and 310C, 69 and 340C, 931 and 576W/m2, respectively, while a cloudy day was eight and -30C and 861.2 and 449W/m2 respectively. Clear sky day maximum and the average mass flow rate was 0.25 and 0.21 Kg/sec, while the cloudy flow rate was 0.27 and 0.03 Kg/sec, respectively. A clear sky has a heat loss of -0.307 and -0.05kW for a cloudy day. R square were 0.76 and 0.4 for clear sky and cloudy day, respectively. For the heat and radiation correlation relationship, a clear sky has a slope of 0.0029 and 0.0539 for a cloudy day. In other conditions, the solar radiation per unit area increases by 1 W/m2, and the daily heat is collected. Increasing A W/m2, the temperature difference between the average temperature of the hot water storage tank and the average temperature of the environment increases by one °C, and the daily heat collection decreases A W/m2. It concludes that a clear sky has much better efficiency than a cloudy day.

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Development of a Linear Regression Model Based on the Most Influential Predictors for a Research Office Cooling Load

Cited by 10 publications

References 30 publications

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

Estimating the Potential of Biogas Yield from Anaerobic Co-digestion of Organic Waste with Ensemble Machine learning

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

Weather influence on the performance of solar collector

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