An ensemble approach for short-term load forecasting by extreme learning machine

Song, Li; Goel, L.; Wang, Peng

doi:10.1016/j.apenergy.2016.02.114

Cited by 220 publications

(111 citation statements)

References 37 publications

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“…Several ensemble-based forecasting models can be found in the literature [28][29][30][31][32][33]. For most of the previously proposed models, the final forecast is made as a simple or weighted average of the output of the participating algorithms, i.e., perform a collaborative or voted decision.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane¹,

Woon²,

Aung³

2016

Preprint

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Day-ahead forecasting of electricity prices is important in deregulated electricity markets for all the stakeholders: energy wholesalers, traders, retailers, and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participate in predicting the price for each hour of a day. We propose two different strategies, namely, Fixed Weight Method (FWM) and Varying Weight Method (VWM), for selecting each hour's expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. The proposed ensemble model offers better results than both the Pattern Sequence-based Forecasting (PSF) method and our own previous work using Artificial Neural Networks (ANN) alone do on the datasets for New York, Australian, and Spanish electricity markets.

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Section: Related Workmentioning

confidence: 99%

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Neupane¹,

Woon²,

Aung³

2016

Preprint

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“…Therefore, the PIs are more valuable and informative for decision makers to make well preparation for the worst and the best possible condition ahead [18]. Indeed, the PIs have recently become a popular tool to cover different uncertainties in power systems [19][20][21], such as electricity price [18], wind power forecasting [19], and short-term load forecasting [19]. For solar power forecasting, several PIs construction approaches have been developed.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Zhuang

Sheng

et al. 2017

Energies

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High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

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“…Therefore, there is a high demand for the generation, transmission and sales of electricity, because excess supply can result in wasted energy resources and in case of excess demand the need for electricity cannot be satisfied. Therefore, performing load forecasting based on the historical data has been a basic task in the operation of electric systems [2]. With the rapid development of society and continuous improvement of economic levels, people have gradually shown a higher desire for electricity, which poses a huge challenge to the forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

Dong

et al. 2016

Energies

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Accurate short-term electrical load forecasting plays a pivotal role in the national economy and people's livelihood through providing effective future plans and ensuring a reliable supply of sustainable electricity. Although considerable work has been done to select suitable models and optimize the model parameters to forecast the short-term electrical load, few models are built based on the characteristics of time series, which will have a great impact on the forecasting accuracy. For that reason, this paper proposes a hybrid model based on data decomposition considering periodicity, trend and randomness of the original electrical load time series data. Through preprocessing and analyzing the original time series, the generalized regression neural network optimized by genetic algorithm is used to forecast the short-term electrical load. The experimental results demonstrate that the proposed hybrid model can not only achieve a good fitting ability, but it can also approximate the actual values when dealing with non-linear time series data with periodicity, trend and randomness.

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An ensemble approach for short-term load forecasting by extreme learning machine

Cited by 220 publications

References 37 publications

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

Ensemble Prediction Model with Expert Selection for Electricity Price Forecasting

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Research and Application of a Hybrid Forecasting Model Based on Data Decomposition for Electrical Load Forecasting

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