Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings

Tran, Duc-Hoc; Luong, Duc-Long; Chou, Jui‐Sheng

doi:10.1016/j.energy.2019.116552

Cited by 84 publications

(24 citation statements)

References 57 publications

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“…Ensemble methods have gained substantial attention in recent years and are extensively used nowadays because of their favourable forecasting predictive performance, and the combination of models may contribute in avoiding overfitting, which can occur by the selection of the best model in a single model scenario [1]. Studies that used ensemble methods as energy demand prediction methods for buildings may be found in [41,42].…”

Section: Figurementioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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Section: Figurementioning

confidence: 99%

Design of Ensemble Forecasting Models for Home Energy Management Systems

et al. 2021

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“…As an initial stage for grouping, the representative load pattern of each household energy consumption was derived from median value of hourly energy consumption [27]. Normalization is performed using the maximum and minimum normalization of the representative load pattern for each household [28]:…”

Section: The Methods For Clustering Of Household Load Shapementioning

confidence: 99%

Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

et al. 2021

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Since January 2020, the COVID-19 pandemic has been impacting various aspects of people’s daily lives and the economy. The first case of COVID-19 in South Korea was identified on 20 January 2020. The Korean government implemented the first social distancing measures in the first week of March 2020. As a result, energy consumption in the industrial, commercial and educational sectors decreased. On the other hand, residential energy consumption increased as telecommuting work and remote online classes were encouraged. However, the impact of social distancing on residential energy consumption in Korea has not been systematically analyzed. This study attempts to analyze the impact of social distancing implemented as a result of COVID-19 on residential energy consumption with time-varying reproduction numbers of COVID-19. A two-way fixed effect model and demographic characteristics are used to account for the heterogeneity. The changes in household energy consumption by load shape group are also analyzed with the household energy consumption model. There some are key results of COVID-19 impact on household energy consumption. Based on the hourly smart meter data, an average increase of 0.3% in the hourly average energy consumption is caused by a unit increase in the time-varying reproduction number of COVID-19. For each income, mid-income groups show less impact on energy consumption compared to both low-income and high-income groups. In each family member, as the number of family members increases, the change in electricity consumption affected by social distancing tends to decrease. For area groups, large area consumers increase household energy consumption more than other area groups. Lastly, The COVID-19 impact on each load shape is influenced by their energy consumption patterns.

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“…Tran et al [27] used real data from residential buildings to develop an evolutionary Neural Machine Inference Model (ENMIM) for energy consumption prediction. Their new ensemble model integrates two single supervised learning machines: the Least Squares Support Vector regression (LSSVR) and the Radial Basis Function Neural Network (RBFNN).…”

Section: Related Workmentioning

confidence: 99%

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

et al. 2021

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The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg–Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.

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Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings

Cited by 84 publications

References 57 publications

Design of Ensemble Forecasting Models for Home Energy Management Systems

Design of Ensemble Forecasting Models for Home Energy Management Systems

Empirical Analysis of the Impact of COVID-19 Social Distancing on Residential Electricity Consumption Based on Demographic Characteristics and Load Shape

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

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