Gated ensemble learning method for demand-side electricity load forecasting

Burger, Eric M.; Moura, Scott J.

doi:10.1016/j.enbuild.2015.10.019

Cited by 47 publications

(24 citation statements)

References 28 publications

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“…Table 11 presents some important papers in this field. Burger and Moura (2015) [75] worked on the generalization of electricity demand forecasting by formulating an ensemble learning method to perform model validation. By learning from data streams of electricity demand, this method needed little information about energy end use, which made it desirable for real utilization.…”

Section: Ensemble Methodsmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: Ensemble Methodsmentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…Furthermore, Evolutionary Algorithms have been applied to short-term forecasting energy demand by Castelli et al in [38,39]. Burger and Moura [40] tackled the forecasting of electricity demand by applying an ensemble learning approach that uses Ordinary Least Squares and k-Nearest Neighbors. In [41], Papadopoulos and Karakatsanis explore the ensemble learning approach and compare four dfferent mehtods: seasonal autoregressive moving average (SARIMA), seasonal autoregressive moving average with exogenous variable (SARIMAX), random forests (RF) and gradient boosting regression trees (GBRT).…”

Section: Related Workmentioning

confidence: 99%

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

et al. 2018

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The ability to predict short-term electric energy demand would provide several benefits, both at the economic and environmental level. For example, it would allow for an efficient use of resources in order to face the actual demand, reducing the costs associated to the production as well as the emission of CO 2 . To this aim, in this paper we propose a strategy based on ensemble learning in order to tackle the short-term load forecasting problem. In particular, our approach is based on a stacking ensemble learning scheme, where the predictions produced by three base learning methods are used by a top level method in order to produce final predictions. We tested the proposed scheme on a dataset reporting the energy consumption in Spain over more than nine years. The obtained experimental results show that an approach for short-term electricity consumption forecasting based on ensemble learning can help in combining predictions produced by weaker learning methods in order to obtain superior results. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that using an ensemble scheme can achieve very accurate predictions, and thus that it is a suitable approach for addressing the short-term load forecasting problem.

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“…There are two main types of individual models-classical forecasting models that analyze past electricity consumption at the engineering level such as exponential smoothing [7], ARIMA [8], state space models [9], grey models [10] and linear regression [11], and machine learning models such as artificial neural-networks (ANN) [12], support vector regression [13], Gaussian processes [14] and ensemble learning methods [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

Yang

et al. 2019

Sustainability

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(1) Background: Electricity consumption data are often made up of complex, unstable series that have different fluctuation characteristics in different industries. However, electricity demand forecasting is a prerequisite for the control and scheduling of power systems. (2) Methods: As most previous research has focused on prediction accuracy rather than stability, this paper developed a decomposition-based combination forecasting model using dynamic adaptive entropy-based weighting for total electricity demand forecasting at the engineering level. (3) Results: To further illustrate the prediction accuracy and stationarity of the proposed method, a comparison analysis using an analysis of variance and an orthogonal approach to solve the least squares equations was conducted using classical individual models, a combination forecasting model, and a decomposition-based combination forecasting model. The proposed method had a very satisfactory overall performance with good verification and validation compared to autoregressive integrated moving average (ARIMA) and artificial neural-networks (ANN). (4) Conclusion: As the proposed method dynamically combines various forecast models and can decompose and adapt to various characteristic data sets, it was found to have an accurate, stable forecast performance. Therefore, it could be broadly applied to forecasting electricity demand and developing electricity generation plans and related energy policies.

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Gated ensemble learning method for demand-side electricity load forecasting

Cited by 47 publications

References 28 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

Decomposition-Based Dynamic Adaptive Combination Forecasting for Monthly Electricity Demand

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