Energy Consumption Prediction in Low Energy Buildings using Machine learning and Artificial Intelligence for Energy Efficiency

Vijayan, Priya

doi:10.1109/iyce54153.2022.9857548

Cited by 10 publications

(2 citation statements)

References 6 publications

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“…Using a gradient descent algorithm and a cross-validation approach to construct a type-2 fuzzy wavelet neural network (T2-FWNN) system with high accuracy, Abiyev et al, 2023 [79] predicted the energy demand in residential buildings. Using MATLAB to execute their research, Vijayan P. [80] used linear regression (LR), SVM, free tree (FR), the ensemble model and ANN models for energy forecasting. A process for selecting the most suitable model for specific areas using data from the Kaggle data center and experimental data was also used to create regression models of appliances' energy use in low-energy buildings.…”

Section: For Energy Management and Energy Consumption Predictionmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

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Section: For Energy Management and Energy Consumption Predictionmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

View full text Add to dashboard Cite

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“…The modelling method offered in (Almalaq et al 2019) is established upon deep learning and GA for enhancing the prediction accuracy of LSTM. (Vijayan et al 2022) compares the results of various machine learning and deep learning models, then establishes the dependencies between energy consumption and parameters such as temperature and wind speed. (Clayton et al 2022) builds a hybrid energy consumption prediction model combining RF and ensemble deep learning methods, achieving accurate prediction and reducing prediction errors.…”

Section: Introductionmentioning

confidence: 99%

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

Su,

Duan,

Zhuang

et al. 2023

Int. J. Renew. Energy Dev.

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The accurate prediction of building energy consumption is a crucial prerequisite for demand response (DR) and energy efficiency management of buildings. Nevertheless, the thermal inertia and probability distribution characteristics of energy consumption are frequently ignored by traditional prediction methods. This paper proposes a building energy consumption prediction method based on Bayesian regression and thermal inertia correction. The thermal inertia correction model is established by introducing an equivalent temperature variable to characterize the influence of thermal inertia on temperature. The equivalent temperature is described as a linear function of the actual temperature, and the key parameters of the function are optimized through genetic algorithm (GA). Using historical energy usage, temperature, and date type as inputs and future building energy comsuption as output, a Bayesian regression prediction model is established. Through Bayesian inference, combined with prior information on building energy usage data, the posterior probability distribution of building energy usage is inferred, thereby achieving accurate forecast of building energy consumption. The case study is conducted using energy consumption data from a commercial building in Nanjing. The results of the case study indicate that the proposed thermal inertia correction method is effective in narrowing the distribution of temperature data from a range of 24.5°C to 36.5°C to a more concentrated range of 26.5°C to 34°C, thereby facilitating a more focused and advantageous data distribution for predictions. Upon applying the thermal inertia correction method, the relative errors of the Radial Basis Function (RBF) and Deep Belief Network (DBN) decreases by 2.0% and 3.1% respectively, reaching 10.9% and 7.0% correspondingly. Moreover, with the utilization of Bayesian regression, the relative error further decreases to 4.4%. Notably, the Bayesian regression method not only achieves reduced errors but also provides probability distribution, demonstrating superiority over traditional methods.

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Vibration Energy Harvesting: A Bibliometric Analysis of Research Trends and Challenges

Al-Quaishi,

Lu,

Alani

2024

J. Vib. Eng. Technol.

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Energy Consumption Prediction in Low Energy Buildings using Machine learning and Artificial Intelligence for Energy Efficiency

Cited by 10 publications

References 6 publications

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

Vibration Energy Harvesting: A Bibliometric Analysis of Research Trends and Challenges

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