City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique

Jahani, Elham; Cetin, Kristen; Cho, In Ho

doi:10.1016/j.buildenv.2020.106667

Cited by 34 publications

(18 citation statements)

References 35 publications

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“…Most existing research is aimed at either office or residential buildings. Due to the many different types and uses for buildings, there are great differences in occupant behaviour [34]. The remainder of this article is arranged as follows: Section 2 introduces the data source and preprocessing.…”

Section: Introductionmentioning

confidence: 99%

Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization

Zhang

Cong

Guo

et al. 2022

J. Phys.: Conf. Ser.

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This study aims to improve the real-time accuracy of cooling load forecasting for heating, ventilating and air-conditioning systems (HVAC). This article takes the cooling load in a study room in Qingdao, China, which has been put into use for the first time, as the research object, and establishes a TRNSYS simulation platform to obtain sufficient load data. After using the mean influence value (MIV) and Spearman correlation coefficient to screen the characteristic variables, a hybrid algorithm (CS-CPSO) based on cuckoo search (CS) and particle swarm optimization (PSO) is proposed. Firstly, the iterative extremum is introduced to PSO, secondly, mechanism of levy random flight to generate random new nest in CS is used to initialize PSO particles adaptively, Finally, the optimization algorithm is applied to optimize the back propagation (BP) and support vector regression (SVR) load training models (WBP, WSVR, RBP, RSVR) of the working day (W) and rest day (R), respectively. The maximum grey correlation coefficient is utilized to establish the both models (CS-CPSO-CW, CS-CPSO-CR) of the working day (W) and rest day (R) based on CS-CPSO. In this way, the forecasting results are optimized and then compared with the regression prediction method. The analysis shows that the accuracy of the optimized BP model and SVR model are improved and fully considering the differences, the accuracy of the cooling load prediction is effectively promoted by separately, optimal selection between the prediction values of advanced models (CS-CPSO-WBP, CS-CPSO-WSVR and CS-CPSO-RBP, CS-CPSO-RSVR) gives full play to each algorithm’s advantages and makes up for their shortcomings, and it greatly increases reliability and improves accuracy, which in turn provides the basis for the optimal plan, control, and operation of the HVAC.

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Section: Introductionmentioning

confidence: 99%

Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization

Zhang

Cong

Guo

et al. 2022

J. Phys.: Conf. Ser.

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“…Some of these have promoted urban systems design [21], which are utilized for enabling technologies and various techniques [22] [23] [24] in the development of the built environments. Amongst many examples, some of these technologies include Internet of Things (IoT) [25] [26] [27] [28], internet+ platforms [29] [30] [31], the use and the integration of artificial intelligence (AI) [32] and informatics from communication technology views [33] [34] [35], such as big data, the application of cybersecurity [36], algorithm-based methods [37] [38] [39] [40], machine learning (ML) deep learning (DL), cognitive computing and big data analytics [20], automation methods [41], etc. So far, the primary challenges for these have been the integration of the so-called urban innovation through policies [42] [43], adaptability to the use or integration with our existing networks [44], acceptance of information technologies [45], and financing of those high-level technological initiatives [46] that are transformative in many ways.…”

Section: Introductionmentioning

confidence: 99%

From Transitions to Transformation: A Brief Review of the Potential Impacts of COVID-19 on Boosting Digitization, Digitalization, and Systems Thinking in the Built Environment

Cheshmehzangi¹

2021

JBCPR

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In a short time, during the early phases of the COVID-19 pandemic outbreak, we managed to shift rapidly to use digital technologies and replace some of our daily operations with virtual modes. This shift happened so instantly and widely that it enables us to argue that the COVID-19 became a valid reason to boost some of the gradual and ongoing transitions towards faster transformations. In this study, we use gray literature to delve into arguments around the boost for digitization, digitalization, and systems thinking in the development of the built environment. This is mostly discussed from the influence of COVID-19 on some of the existing practices or the business-as-usual of the built environment sector. From technological advancement to technology use, these arguments are put forward to discuss what is likely to be the major driver of technological adoption and the shifting paradigms that are yet to be revealed. The study concludes that the current push towards new directions and development pathways are likely to be widely accepted in a shorter time. The findings of this brief study feed into existing arguments on transformative pathways due to the COVID-19 pandemic.

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“…Huang et al (2021) applied a deep learning method in developing an energy management system [ 8 ]. Jahani et al (2020) developed a prediction model by integrating a genetic algorithm and numerical moment matching method to predict energy consumption in residential buildings [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Time‐Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

Truong

Ngo

Pham

2021

Computational Intelligence and Neuroscience

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Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

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City-scale single family residential building energy consumption prediction using genetic algorithm-based Numerical Moment Matching technique

Cited by 34 publications

References 35 publications

Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization

Research on Combined Forecasting of Cooling Load Based on Advanced Cuckoo Search and Improved Particle Swarm Optimization

From Transitions to Transformation: A Brief Review of the Potential Impacts of COVID-19 on Boosting Digitization, Digitalization, and Systems Thinking in the Built Environment

Forecasting Time‐Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

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