Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods

Feng, Yanxiao; Duan, Qiuhua; Chen, Xi; Yakkali, Sai Santosh; Wang, Julian

doi:10.1016/j.apenergy.2021.116814

Cited by 53 publications

(13 citation statements)

References 37 publications

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“…For example, in [59] XGBoost outperformed six other methods including support vector regression (SVR) and elastic net regression in terms of feature extraction and prediction accuracy for day-ahead building cooling load profiles. XGBoost also outperformed all eight other models including SVR when predicting domestic space cooling in [60]. XGBoost also performed best against five other models in a study exploring household CO2 emission patterns and the underlying drivers [61].…”

Section: Extreme Gradient Boostingmentioning

confidence: 90%

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 – March 2022

Zapata-Webborn¹,

Pullinger²,

McKenna³

et al. 2023

Preprint

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The COVID-19 pandemic changed the way many people lived, worked, and studied around the world, both during and after the lockdowns. Changes to daily routines affected domestic electricity and gas use. While early studies estimated the impact of the first national lockdown, the long-term effects remain under-researched. In this paper we analyse how domestic electricity and gas consumption changed in the two years since the first UK lockdown in terms of both total demand and timing of demand. We develop counterfactual (predictive) models using elastic net regression, neural networks, and extreme gradient boosting and compare observed energy use with predicted use given weather and calendar variables for each household (508 for electricity, 326 for gas). We apply cluster analysis to identify common daily energy demand profiles and observe the changes in the proportions of households in each cluster for 3540 (electricity) and 2850 (gas) households between January 2020 and March 2022. We compare the results for different subsamples, such as those with and without children or working adults, households with different levels of financial wellbeing, and households in different Energy Performance Certificate (EPC) bands. We find that the pandemic increased electricity consumption throughout the two-year period, and increased gas consumption during the winter lockdowns. Demand profiles for weekdays became more similar to those on weekends for households with children or with adults in work. On average electricity consumption was still around 5% higher than predicted at the start of 2022, largely due to increased use in households with children. On average, gas consumption was lower than predicted during winter 2021/22, which may be attributable to rising gas prices.

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Section: Extreme Gradient Boostingmentioning

confidence: 90%

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 – March 2022

Zapata-Webborn¹,

Pullinger²,

McKenna³

et al. 2023

Preprint

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“…Unlike gradient boosting, XGBoost uses a second-order Taylor expansion in its loss function and incorporates the learning results from previous iterations to weigh each sample during the current training phase (Nasiri et al [12], Sairam et al [51]). Numerous studies have demonstrated that the XGBoost method is a highly accurate and flexible AI predictor tool (Feng et al [52], Nasiri et al [12], Alsahaf et al [53]). Compared with other conventional machine learning approaches, XGBoost offers a more extensive range of hyperparameters, making it better tuned and capable of achieving superior performance.…”

Section: Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

Uncovering the Impact of Local and Global Interests in Artists on Stock Prices of K-Pop Entertainment Companies: A SHAP-XGBoost Analysis

Choi

2023

Axioms

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Stock price prediction is a significant area of research in finance that has been ongoing for a long time. Several mathematical models have been utilized in this field to predict stock prices. However, recently, machine learning techniques have demonstrated remarkable performance in stock price prediction. Moreover, XAI (explainable artificial intelligence) methodologies have been developed, which are models capable of interpreting the results of machine learning algorithms. This study utilizes machine learning to predict stock prices and uses XAI methodologies to investigate the factors that influence this prediction. Specifically, we investigated the relationship between the public’s interest in artists affiliated with four K-Pop entertainment companies (HYBE, SM, JYP, and YG). We used the Naver Keyword Trend and Google Trend index data for the companies and their representative artists to measure local and global interest. Furthermore, we employed the SHAP-XGBoost model to show how the local and global interest in each artist affects the companies’ stock prices. SHAP (SHapley Additive exPlanations) and XGBoost are models that show excellent results as XAI and machine learning methodologies, respectively. We found that SM, JYP, and YG are highly correlated, whereas HYBE is a major player in the industry. YG is influenced by variables from other companies, likely owing to HYBE being a major shareholder in YG’s subsidiary music distribution company. The influence of popular artists from each company was significant in predicting the companies’ stock prices. Additionally, the foreign ownership ratio of a company’s stocks affected the importance of Google Trend and Naver Trend indexes. For example, JYP and SM had relatively high foreign ownership ratios and were influenced more by Google Trend indexes, whereas HYBE and YG were influenced more by Naver Trend indexes. Finally, the trend indexes of artists in SM and HYBE had a positive correlation with stock prices, whereas those of YG and JYP had a negative correlation. This may be due to steady promotions and album releases from SM and HYBE artists, while YG and JYP suffered from negative publicity related to their artists and executives. Overall, this study suggests that public interest in K-Pop artists can have a significant impact on the financial performance of entertainment companies. Moreover, our approach offers valuable insights into the dynamics of the stock market, which makes it a promising technique for understanding and predicting the behavior of entertainment stocks.

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“…2020 ), HVAC optimization (Li 2020 ), Space cooling load forecasting (Feng et al. 2021 ), load disaggregation and monitoring (Xiao et al. 2021 ), water monitoring (Somontina et al.…”

Section: Overview Of Ai-big Data Analytic Frameworkmentioning

confidence: 99%

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

et al. 2022

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In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

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Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods

Cited by 53 publications

References 37 publications

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 – March 2022

The impact of COVID-19 on household energy consumption in England and Wales from April 2020 – March 2022

Uncovering the Impact of Local and Global Interests in Artists on Stock Prices of K-Pop Entertainment Companies: A SHAP-XGBoost Analysis

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

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