Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

Nichiforov, Cristina; Stâmâtescu, Grigore; Stamatescu, Iulia; Făgărăşan, Ioana

doi:10.3390/info10060189

Cited by 17 publications

(4 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Year Application [17] 2016 Stock market [83] 2016 Electricity load [84] 2016 Traffic flow [19] 2017 Stock prices [85,86] 2017 Stock market [87] 2017 Electricity load [88] 2017 Air quality [26] 2018 Forecasting Cancer Growth [89,90] 2018 Stock market [20] 2018 Stock prices [7] 2018 Electricity price [24] 2018 Diabetes mellitus [91] 2018 Rainfall-runoff modelling [92] 2018 Predicting water table depth [93,94] 2018 Electricity load [33] 2018 Life prediction of batteries [10] 2018 Solar power and electricity load [95] 2018 Solar intensity [96] 2018 Air quality [97] 2019 UCI data sets [98] 2019 Building load [31] 2019 Petroleum production [14] 2019 Monthly precipitation [99] 2019 Weather forecasting Table 5. Cont.…”

Section: Refmentioning

confidence: 99%

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro,

Capone,

Iannuzzo

et al. 2023

Information

View full text Add to dashboard Cite

A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers.

show abstract

Section: Refmentioning

confidence: 99%

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro,

Capone,

Iannuzzo

et al. 2023

Information

View full text Add to dashboard Cite

show abstract

“…e following year, the same authors in [95] also experimented with the same dataset and the same RNN architectures, adding to their research one more building located in New York. Useful conclusions extracted from both works were the following: RNN architecture was a good candidate, prompting promising accuracy results for building load forecasting.…”

Section: Commercial Building Loadmentioning

confidence: 99%

A Survey on Deep Learning for Building Load Forecasting

Patsakos

Vrochidou

Papakostas

2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Energy consumption forecasting is essential for efficient resource management related to both economic and environmental benefits. Forecasting can be implemented through statistical analysis of historical data, application of Artificial Intelligence (AI) algorithms, physical models, and more, and focuses on two directions: the required load for a specific area, e.g., a city, and the required load for a building. Building power forecasting is challenging due to the frequent fluctuation of the required electricity and the complexity and alterability of each building’s energy behavior. This paper focuses on the application of Deep Learning (DL) methods to accurately predict building (residential, commercial, or multiple) power consumption, by utilizing the available historical big data. Research findings are compared to state-of-the-art statistical models and AI methods of the literature to comparatively evaluate their efficiency and justify their future application. The aim of this work is to review up-to-date proposed DL approaches, to highlight the current research status, and to point out emerging challenges and future potential directions. Research revealed a higher interest in residential building load forecasting covering 47.5% of the related literature from 2016 up to date, focusing on short-term forecasting horizon in 55% of the referenced papers. The latter was attributed to the lack of available public datasets for experimentation in different building types, since it was found that in the 48.2% of the related literature, the same historical data regarding residential buildings load consumption was used.

show abstract

“…There have been studies on data-driven methods that have focused on energy consumption [52][53][54][55][56][57][58], indoor thermal comfort [59], electricity utilization [60][61][62][63][64][65][66][67][68], photovoltaic generation for the building [69], electricity and heat demand [70], cooling load [71][72][73][74], heating and cooling load [75], occupancy and energy consumption [76], clustering energy consumption [77] and peak load demand [78]. Based on the aforementioned studies, a summary of their contributions and limitations is presented in Table 2.…”

Section: ] Yumentioning

confidence: 99%

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

et al. 2020

View full text Add to dashboard Cite

Buildings are among the largest energy consumers in the world. As new technologies have been developed, great advances have been made in buildings, turning conventional buildings into smart buildings. These smart buildings have allowed for greater supervision and control of the energy resources within the buildings, taking steps to energy management strategies to achieve significant energy savings. The forecast of energy consumption in buildings has been a very important element in these energy strategies since it allows adjusting the operation of buildings so that energy can be used more efficiently. This paper presents a review of energy consumption forecasting in smart buildings for improving energy efficiency. Different forecasting methods are studied in nonresidential and residential buildings. Following this, the literature is analyzed in terms of forecasting objectives, input variables, forecasting methods and prediction horizon. In conclusion, the paper examines future challenges for building energy consumption forecasting.

show abstract

Evaluation of Sequence-Learning Models for Large-Commercial-Building Load Forecasting

Cited by 17 publications

References 23 publications

Deep Learning for Time Series Forecasting: Advances and Open Problems

Deep Learning for Time Series Forecasting: Advances and Open Problems

A Survey on Deep Learning for Building Load Forecasting

A Review of Energy Consumption Forecasting in Smart Buildings: Methods, Input Variables, Forecasting Horizon and Metrics

Contact Info

Product

Resources

About