User behaviour models to forecast electricity consumption of residential customers based on smart metering data

Lazzari, Florencia; Mor, Gerard; Cipriano, Jordi; Gabaldon, Eloi; Grillone, Benedetto; Chemisana, Daniel; Solsona, Francesc

doi:10.1016/j.egyr.2022.02.260

Cited by 29 publications

(9 citation statements)

References 30 publications

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“…Therefore, it is crucial to conduct research in this field using data from fiscal smart meters that include integrated local weather data. A combination of several ML techniques to manage data inconsistencies from the fiscal smart meter modeling user behavior and forecast individual users' residential electricity consumption was conceived [14]. Such studies focus on forecasting the next day's hourly energy consumption of residential households [60].…”

Section: Challenges In Household Energy Forecastingmentioning

confidence: 99%

“…Extreme gradient-boosting regression (XGBR) is an optimized implementation of GB, usually used with decision trees [14,69]. This efficient, fast, and scalable machine learning method is frequently used along with neural networks.…”

Section: Tree-based and Boosting Modelsmentioning

confidence: 99%

“…In the past, it was a usual practice to forecast residential energy usage by estimating the hourly average electricity usage of many households [14]. This gives the ability to predict peak electricity demand times and allows power system operators to switch on additional power plants to avoid severe electrical shortages.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Koukaras,

Mustapha,

Mystakidis

et al. 2024

Energies

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The building sector, known for its high energy consumption, needs to reduce its energy use due to rising greenhouse gas emissions. To attain this goal, a projection for domestic energy usage is needed. This work optimizes short-term load forecasting (STLF) in the building sector while considering several variables (energy consumption/generation, weather information, etc.) that impact energy use. It performs a comparative analysis of various machine learning (ML) models based on different data resolutions and time steps ahead (15 min, 30 min, and 1 h with 4-step-, 2-step-, and 1-step-ahead, respectively) to identify the most accurate prediction method. Performance assessment showed that models like histogram gradient-boosting regression (HGBR), light gradient-boosting machine regression (LGBMR), extra trees regression (ETR), ridge regression (RR), Bayesian ridge regression (BRR), and categorical boosting regression (CBR) outperformed others, each for a specific resolution. Model performance was reported using R2, root mean square error (RMSE), coefficient of variation of RMSE (CVRMSE), normalized RMSE (NRMSE), mean absolute error (MAE), and execution time. The best overall model performance indicated that the resampled 1 h 1-step-ahead prediction was more accurate than the 15 min 4-step-ahead and the 30 min 2-step-ahead predictions. Findings reveal that data preparation is vital for the accuracy of prediction models and should be model-adjusted.

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Section: Challenges In Household Energy Forecastingmentioning

confidence: 99%

Section: Tree-based and Boosting Modelsmentioning

confidence: 99%

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Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Koukaras,

Mustapha,

Mystakidis

et al. 2024

Energies

View full text Add to dashboard Cite

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“…18 The patterns of residential consumption were divided into a three-level structure considering the seasons change based on the data collected throughout the day by smart meters. The study by Lazzari et al 19 presented a technique to predict the energy consumption for residential buildings, where the Gaussian mixture clustering and the extreme gradient boosting classification techniques were utilized to identify and estimate customer behavior. The obtained clusters were added to ANN to enable an improved forecast of electricity consumption.…”

Section: State Of the Artmentioning

confidence: 99%

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

Ramadan

Huang

Bamisile

et al. 2023

Energy Science & Engineering

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Nowadays, the load monitoring system is an important element in smart buildings to reduce energy consumption. Nonintrusive load monitoring (NILM) is utilized to determine the power consumption of each appliance in smart homes. The main problem of NILM is how to separate each appliance's power from the signal of aggregated consumption. In this regard, this paper presents a combination between particle swarm optimization (PSO) and artificial neural networks (ANNs) to identify electrical appliances for demand‐side management. ANN is applied in NILM as a load identification task, and PSO is used to train the ANN algorithm. This combination enhances the NILM technique's accuracy, which is further verified by experiments on different datasets like Reference Energy Disaggregation Dataset, UK Domestic Appliance‐Level ElectricityUK‐DALE, and Indian data for Ambient Water and electricity Sensing. The high accuracy of the proposed algorithm is verified by comparisons with state of the art methods. Compared with other approaches, the total mean absolute error has decreased from 39.3566 to 18.607. Also, the normalized root mean square error (NRMSE) method has been used to compare the measured and predicted results. The NRMSE is in the range of 1.719%–16.514%, which means perfect consistency. This demonstrates the effectiveness of the proposed approach for home energy management. Furthermore, customer behavior has been studied, considering energy costs during day hours.

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“…The existing literature on residential consumers predominantly concentrates on constructing precise forecast models tailored to individual households [5][6][7]. However, due to the unpredictable nature of consumption behavior, it becomes a challenge for a single model to accurately predict the load of different households.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Top-Down Load Forecasting Model for Residential Consumers

Parkash,

Lie,

et al. 2024

Energies

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This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.

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User behaviour models to forecast electricity consumption of residential customers based on smart metering data

Cited by 29 publications

References 30 publications

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Optimizing Building Short-Term Load Forecasting: A Comparative Analysis of Machine Learning Models

Towards energy‐efficient smart homes via precise nonintrusive load disaggregation based on hybrid ANN–PSO

End-to-End Top-Down Load Forecasting Model for Residential Consumers

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