A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns

Zhang, Xiaobo; Wang, Jianzhou

doi:10.1016/j.asoc.2018.01.017

Cited by 87 publications

(27 citation statements)

References 49 publications

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“…These methods have been used traditionally and are still used. Zhang and Wang [26] use similar techniques by developing decomposition-ensemble models.…”

Section: Forecasting For Special Eventsmentioning

confidence: 99%

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

2019

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Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt–Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt–Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt–Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.

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“…These methods have been used traditionally and are still used. Zhang and Wang [26] use similar techniques by developing decomposition-ensemble models.…”

Section: Forecasting For Special Eventsmentioning

confidence: 99%

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

2019

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“…Here, ci(t) and ωi(t) are real-time quantities whose values change instantaneously. The Hilbert transform assisted Huang EMD preprocessor, expressed in (10), is also called the Hilbert-Huang transform (HHT).…”

Section: A Hilbert-huang Transform (Hht)mentioning

confidence: 99%

“…The existing electricity prediction approaches can be categorized based on the models they use as time-series and regression models. The time-series approaches describe future electricity demand based on its previous and present time series features [6]- [10]. The regression (causal) approaches characterize electricity demand based on external features that can possibly affect the electricity consumption [11] - [18].…”

Section: Introductionmentioning

confidence: 99%

Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management

Eseye

Lehtonen

Tukia

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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The electricity consumption profile of buildings are different from the typical load curves that represent the electricity consumption of large systems at the national or regional level. The electricity demand in buildings is many times lower than the region-or nationwide demands. It is also much more volatile and stochastic, meaning that the conventional tools are not effective enough for straightforward application at a building demand level. In this paper, an integrated approach consisting of Hilbert-Huang Transform (HHT), Regrouping Particle Swarm Optimization (RegPSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) is devised for 24h-ahead prediction of electric power consumption in buildings. The forecasts are used as input information for smart decisions of distributed energy management systems that control the optimal bidding and scheduling of energy resources for building energy communities. The effectiveness of the proposed forecasting approach is demonstrated using actual electricity demand data from various buildings in the Otaniemi area of Espoo, Finland. The prediction performance of the proposed approach for various building types (energy customer clusters), has been examined and statistical comparisons are presented. The prediction results are presented for future days with a one-hour time interval. The validation results demonstrate that the approach is able to forecast the buildings' electricity demand with smaller error, outperforming five other approaches, and in reasonably short computation times.

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“…The randomness of these kinds of energies may bring a impulse to the power grid, thus the thermal power generation requires accurate planning according to the renewable energy generation and social electricity consumption to balance the power generation and consumption. The high randomness of renewable energy generation calls for a more flexible and intelligent scheduling technology to solve the problem [1]. At the same time, the rapid increase in both type and quantity of the electricity consumption leads to drastic variation in the electrical To cope with the above problems with LSTM, in this paper, a deep ensemble learning model is proposed which is based on LSTM within active learning [28,29] framework under the following principles:…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework

et al. 2019

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Short term load forecasting (STLF) is one of the basic techniques for economic operation of the power grid. Electrical load consumption can be affected by both internal and external factors so that it is hard to forecast accurately due to the random influencing factors such as weather. Besides complicated and numerous internal patterns, electrical load shows obvious yearly, seasonal, and weekly quasi-periodicity. Traditional regression-based models and shallow neural network models cannot accurately learn the complicated inner patterns of the electrical load. Long short-term memory (LSTM) model features a strong learning capacity to capture the time dependence of the time series and presents the state-of-the-art performance. However, as the time span increases, LSTM becomes much harder to train because it cannot completely avoid the vanishing gradient problem in recurrent neural networks. Then, LSTM models cannot capture the dependence over large time span which is of potency to enhance STLF. Moreover, electrical loads feature data imbalance where some load patterns in high/low temperature zones are more complicated but occur much less often than those in mild temperature zones, which severely degrades the LSTM-based STLF algorithms. To fully exploit the information beneath the high correlation of load segments over large time spans and combat the data imbalance, a deep ensemble learning model within active learning framework is proposed, which consists of a selector and a predictor. The selector actively selects several key load segments with the most similar pattern as the current one to train the predictor, and the predictor is an ensemble learning-based deep learning machine integrating LSTM and multi-layer preceptor (MLP). The LSTM is capable of capturing the short-term dependence of the electrical load, and the MLP integrates both the key history load segments and the outcome of LSTM for better forecasting. The proposed model was evaluated over an open dataset, and the results verify its advantage over the existing STLF models.

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A novel decomposition‐ensemble model for forecasting short‐term load‐time series with multiple seasonal patterns

Cited by 87 publications

References 49 publications

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

Short-term Forecasting of Electricity Consumption in Buildings for Efficient and Optimal Distributed Energy Management

Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework

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