Machine learning‐based energy consumption clustering and forecasting for mixed‐use buildings

Culaba, Alvin B.; Rosario, Aaron Jules R. Del; Ubando, Aristotle T.; Chang, Jo‐Shu

doi:10.1002/er.5523

Cited by 35 publications

(21 citation statements)

References 28 publications

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“…The underestimation of electricity load can put at risk the system's reliability due to insufficient load required to attend the demanding market [32]. In the same way, electricity consumption forecasting models can improve energy efficiency and sustainability in diverse sectors, such as in residential buildings [33][34][35] and in industry [36,37].…”

Section: Related Workmentioning

confidence: 99%

“…Likewise, energy consumption forecasting systems based on ML models have been used in the literature. Culaba et al [33] employed a hybrid system based on clustering and forecasting using K-Means and SVR models, respectively. Deep learning models, such as Convolution Neural Networks (CNN), were employed by Reference [34] for energy consumption forecasts in the context of new buildings with few historical data.…”

Section: Related Workmentioning

confidence: 99%

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Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

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The usage of smart grids is growing steadily around the world. This technology has been proposed as a promising solution to enhance energy efficiency and improve consumption management in buildings. Such benefits are usually associated with the ability of accurately forecasting energy demand. However, the energy consumption series forecasting is a challenge for statistical linear and Machine Learning (ML) techniques due to temporal fluctuations and the presence of linear and non-linear patterns. Traditional statistical techniques are able to model linear patterns, while obtaining poor results in forecasting the non-linear component of the time series. ML techniques are data-driven and can model non-linear patterns, but their feature selection process and parameter specification are a complex task. This paper proposes an Evolutionary Hybrid System (EvoHyS) which combines statistical and ML techniques through error series modeling. EvoHyS is composed of three phases: (i) forecast of the linear and seasonal component of the time series using a Seasonal Autoregressive Integrated Moving Average (SARIMA) model, (ii) forecast of the error series using an ML technique, and (iii) combination of both linear and non-linear forecasts from (i) and (ii) using a a secondary ML model. EvoHyS employs a Genetic Algorithm (GA) for feature selection and hyperparameter optimization in phases (ii) and (iii) aiming to improve its accuracy. An experimental evaluation was conducted using consumption energy data of a smart grid in a one-step-ahead scenario. The proposed hybrid system reaches statistically significant improvements when compared to other statistical, hybrid, and ML approaches from the literature utilizing well known metrics, such as Mean Squared Error (MSE).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The underestimation of electricity load can put at risk the system’s reliability due to insufficient load required to attend the demanding market [ 18 ]. In the same way, electricity consumption forecasting models can improve energy efficiency and sustainability in diverse sectors such as in residential buildings [ 19 , 20 , 21 ] and in industry [ 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

Neto

Oliveira

Bassetto

et al. 2021

Sensors

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The employment of smart meters for energy consumption monitoring is essential for planning and management of power generation systems. In this context, forecasting energy consumption is a valuable asset for decision making, since it can improve the predictability of forthcoming demand to energy providers. In this work, we propose a data-driven ensemble that combines five single well-known models in the forecasting literature: a statistical linear autoregressive model and four artificial neural networks: (radial basis function, multilayer perceptron, extreme learning machines, and echo state networks). The proposed ensemble employs extreme learning machines as the combination model due to its simplicity, learning speed, and greater ability of generalization in comparison to other artificial neural networks. The experiments were conducted on real consumption data collected from a smart meter in a one-step-ahead forecasting scenario. The results using five different performance metrics demonstrate that our solution outperforms other statistical, machine learning, and ensembles models proposed in the literature.

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“…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

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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.

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Machine learning‐based energy consumption clustering and forecasting for mixed‐use buildings

Abstract: Mixed-use buildings contribute to the sustainable development of cities by providing economic, environmental, and social benefits. Energy management of

Cited by 35 publications

References 28 publications

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters

Energy Consumption Forecasting for Smart Meters Using Extreme Learning Machine Ensemble

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

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