Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios

Ferreira, Greicili dos Santos; Santos, Deilson Martins dos; Avila, Sergio; Albani, Vinicius; Orsi, Gustavo Cardoso; Vieira, Pedro Cesar Cordeiro; Rodrigues, Rafael Nilson

doi:10.1016/j.apenergy.2023.120980

Cited by 11 publications

(4 citation statements)

References 39 publications

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“…Time series forecasting has been a hot topic for decades for a wide variety of subjects like stock exchanges [6], traffic jams [7], or smart building energy consumption [2], respectively aiming at more profitability, decreasing traffic jams, and decreasing operating cost while increasing indoor comfort.…”

Section: Related Workmentioning

confidence: 99%

“…These models may also have been made already by software like EnergyPlus, but they can be lost or inadequate after changes during the construction. It is also recommended by the International Performance Measurement and Verification Protocol (IPMVP-2022) to choose efficient building improvement strategies [2]. The modeling process is also opening to a transition from automation to Model Predictive Control (MPC) [3], leading to further accurate energy demand in building operations under the condition of adequate effort to answer big data challenges.…”

Section: Introductionmentioning

confidence: 99%

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Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings

Closson,

Cérin,

Donsez

et al. 2024

Information

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This paper aims to provide discernment toward establishing a general framework, dedicated to data analysis and forecasting in smart buildings. It constitutes an industrial return of experience from an industrialist specializing in IoT supported by the academic world. With the necessary improvement of energy efficiency, discernment is paramount for facility managers to optimize daily operations and prioritize renovation work in the building sector. With the scale of buildings and the complexity of Heating, Ventilation, and Air Conditioning (HVAC) systems, the use of artificial intelligence is deemed the cheapest tool, holding the highest potential, even if it requires IoT sensors and a deluge of data to establish genuine models. However, the wide variety of buildings, users, and data hinders the development of industrial solutions, as specific studies often lack relevance to analyze other buildings, possibly with different types of data monitored. The relevance of the modeling can also disappear over time, as buildings are dynamic systems evolving with their use. In this paper, we propose to study the forecasting ability of the widely used Long Short-Term Memory (LSTM) network algorithm, which is well-designed for time series modeling, across an instrumented building. In this way, we considered the consistency of the performances for several issues as we compared to the cases with no prediction, which is lacking in the literature. The insight provided let us examine the quality of AI models and the quality of data needed in forecasting tasks. Finally, we deduced that efficient models and smart choices about data allow meaningful insight into developing time series modeling frameworks for smart buildings. For reproducibility concerns, we also provide our raw data, which came from one “real” smart building, as well as significant information regarding this building. In summary, our research aims to develop a methodology for exploring, analyzing, and modeling data from the smart buildings sector. Based on our experiment on forecasting temperature sensor measurements, we found that a bigger AI model (1) does not always imply a longer time in training and (2) can have little impact on accuracy and (3) using more features is tied to data processing order. We also observed that providing more data is irrelevant without a deep understanding of the problem physics.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings

Closson,

Cérin,

Donsez

et al. 2024

Information

View full text Add to dashboard Cite

show abstract

“…Many data-driven techniques have been proposed in order to improve the performance of HVAC systems [ 9 ]. These techniques and applications include system fault detection and diagnosis [ 10 , 11 , 12 , 13 ], and building energy consumption prediction [ 14 , 15 ], control, and optimization [ 16 , 17 ]. The key to these applications and technologies lies in the building sensing environment.…”

Section: Introductionmentioning

confidence: 99%

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Li,

Wang,

Liu

et al. 2024

Sensors

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A lack of available information on heating, ventilation, and air-conditioning (HVAC) systems can affect the performance of data-driven fault-tolerant control (FTC) models. This study proposed an in situ selective incremental calibration (ISIC) strategy. Faults were introduced into the indoor air (Ttz1) thermostat and supply air temperature (Tsa) and chilled water supply air temperature (Tchws) sensors of a central air-conditioning system. The changes in the system performance after FTC were evaluated. Then, we considered the effects of the data quality, data volume, and variable number on the FTC results. For the Ttz1 thermostat and Tsa sensor, the system energy consumption was reduced by 2.98% and 3.72% with ISIC, respectively, and the predicted percentage dissatisfaction was reduced by 0.67% and 0.63%, respectively. Better FTC results were obtained using ISIC when the Ttz1 thermostat had low noise, a 7-day data volume, or sufficient variables and when the Tsa and Tchws sensors had low noise, a 14-day data volume, or limited variables.

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“…In September 2020, at the 75th session of the United Nations General Assembly, the Chinese government put forward the strategic goals of achieving a carbon peak in 2030 and carbon neutrality in 2060. With the expansion of the scale and increasing number of buildings, building energy consumption accounts for about 40% of the world's total energy consumption [2]; meanwhile, in China, building energy consumption accounts for about 45% of the country's total energy consumption [3]. Therefore, in order to reduce building energy consumption and reduce carbon emissions, achieving near-zero energy buildings has become the focus of researchers [4,5].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Research on the Near-Zero Energy Consumption of an Office Building in Hefei Based on a Photovoltaic Curtain Wall

Wang,

Wu,

et al. 2023

Sustainability

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The near-zero energy design of a building is linked to the regional climate in which the building is located. On the basis of studying the cavity size and ground height of a photovoltaic curtain wall, the power generation efficiency of the photovoltaic curtain wall under different ground heights is compared in this paper. According to the “Technical Standard for Near-Zero Energy Buildings”, the personnel and lighting of a 12-metre office building in Hefei were parametrically arranged and three design schemes for near-zero energy buildings were proposed. The energy consumption of the benchmark building and the design energy consumption of each scheme were calculated by using the energy consumption simulation software Design Builder V 7.0.0.096; the feasibility of realizing the near-zero energy consumption building by using each scheme was checked. The results show that when the cavity width of the photovoltaic curtain wall of the office building is 70 mm, the cavity heat transfer coefficient is the lowest and the heat insulation of the building is the best. When the height from the ground is 0.7 m, the power generation efficiency of the photovoltaic curtain wall reaches a maximum of 18.39% and the south façade of the building is more suitable for the layout and installation of the photovoltaic curtain wall. The single-façade photovoltaic curtain wall should be combined with a high-efficiency air conditioning system and lighting system; the installation of a photovoltaic rooftop at the same time can meet the design requirements of near-zero energy buildings in hot-summer and cold-winter areas. This paper provides some guidance for exploring the design of near-zero energy office buildings, which is of practical significance.

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Short- and long-term forecasting for building energy consumption considering IPMVP recommendations, WEO and COP27 scenarios

Cited by 11 publications

References 39 publications

Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings

Design of a Meaningful Framework for Time Series Forecasting in Smart Buildings

Study on Sensor Fault-Tolerant Control for Central Air-Conditioning Systems Using Bayesian Inference with Data Increments

Comprehensive Research on the Near-Zero Energy Consumption of an Office Building in Hefei Based on a Photovoltaic Curtain Wall

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