Optimisation of heat distribution in Tomsk City by inter-cluster connections model implemented in P-graph environment

Kuznetsov, Maxim; Kaldybaeva, Botagoz; Tsibulskiy, Svyatoslav; Boldyryev, Stanislav

doi:10.23919/splitech52315.2021.9566473

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, a dataset from smart meters in residential buildings in Tomsk city in Russia was collected. This dataset was published for the first time in 2020 by IEEE DataPort [45], and, since then, it has been used in some research studies, e.g., [46]. The dataset contained sensor data, building attribute features, and weather records during five heating seasons.…”

Section: Database Selectionmentioning

confidence: 99%

Predicting Energy Consumption in Residential Buildings Using Advanced Machine Learning Algorithms

2023

View full text Add to dashboard Cite

The share of residential building energy consumption in global energy consumption has rapidly increased after the COVID-19 crisis. The accurate prediction of energy consumption under different indoor and outdoor conditions is an essential step towards improving energy efficiency and reducing carbon footprints in the residential building sector. In this paper, a PSO-optimized random forest classification algorithm is proposed to identify the most important factors contributing to residential heating energy consumption. A self-organizing map (SOM) approach is applied for feature dimensionality reduction, and an ensemble classification model based on the stacking method is trained on the dimensionality-reduced data. The results show that the stacking model outperforms the other models with an accuracy of 95.4% in energy consumption prediction. Finally, a causal inference method is introduced in addition to Shapley Additive Explanation (SHAP) to explore and analyze the factors influencing energy consumption. A clear causal relationship between water pipe temperature changes, air temperature, and building energy consumption is found, compensating for the neglect of temperature in the SHAP analysis. The findings of this research can help residential building owners/managers make more informed decisions around the selection of efficient heating management systems to save on energy bills.

show abstract

Section: Database Selectionmentioning

confidence: 99%