Two-Stage Machine Learning Framework for Simultaneous Forecasting of Price-Load in the Smart Grid

Victoire, T. Aruldoss Albert; Gobu, B; Jaikumar, Sivakumar; Arulmozhi, N; Kanimozhi, P.; T, Amalraj Victoire

doi:10.1109/icmla.2018.00176

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…In terms of decomposition methods, the most widely used technique is the wavelet transform [17,19,22,24,34,41,49,51,52,89]. Alternatives methods include empirical mode decomposition [32,90], variational mode decomposition [27,48], and singular spectrum analysis [91,92].…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…The most widely used method is the standard MLP [19,20,32,41,42,51,91,92,94,96,97], followed by the adaptive network-based fuzzy inference system (ANFIS) [19,90,95], radial basis function network [20,24,100], and autoregressive models like ARMA or ARIMA [20,22,24,90]. Other models include LSTM [17], linear regression [50], extreme learning machine [22,50], CNN [50], Bayesian neural network [26,99], exponential GARCH [90], echo state neural network [27], Elman neural networks [18], and support vector regressors [20]. It is important to note that in many of the approaches, the hybrid method does not consider a single forecasting model but combines several of them [19,20,24,50,90,97].…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…Just as for the forecasting model, the diversity of the heuristic optimization algorithms is also large. While the most often utilized algorithm is particle swarm optimization [22,48,51,95,96,100], many other approaches are also used: differential evolution [27], genetic algorithm [95], backtracking search [95], deterministic annealing [100], bat algorithm [41], vaporization precipitation-based water cycle algorithm [93], cuckoo search [92,94], or honey bee mating optimization [24].…”

Section: Hybrid Methodsmentioning

confidence: 99%

“…• In many of the existing studies [17][18][19][20][21][22][23] the testing periods are usually too short to yield conclusive results. In some cases, the test datasets are limited to one-week periods [22,[24][25][26][27][28][29][30]; this ignores the problem of special days, e.g. holidays, and is not representative for the performance of the proposed algorithms across a whole year.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Lago,

Marcjasz,

De Schutter

et al. 2020

Preprint

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While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.

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Section: Hybrid Methodsmentioning

confidence: 99%

Section: Hybrid Methodsmentioning

confidence: 99%

Section: Hybrid Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Lago,

Marcjasz,

De Schutter

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Hybrid models are very complex forecasting frameworks that are composed of several algorithms and techniques. Usually, they comprise different models for different components or modules of a forecasting technique, which can be data decomposition [50][51][52][53][54][55], feature selection [52,[56][57][58][59][60], data clustering [59,[61][62][63], some heuristic optimization of hyperparameters [50,55,57,58,[64][65][66][67] or prediction ensemble [68][69][70][71].…”

Section: Hybridmentioning

confidence: 99%

Electricity Spot Price Forecast by Modelling Supply and Demand Curve

Pinhão

Fonseca

2022

Mathematics

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Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Most methods forecast the electricity price itself; this paper gives a new perspective to the field by trying to forecast the dynamics behind the electricity price: the supply and demand curves originating from the auction. Given the complexity of the data involved which include many block bids/offers per hour, we propose a technique for market curve modeling and forecasting that incorporates multiple seasonal effects and known market variables, such as wind generation or load. It is shown that this model outperforms the benchmarked ones and increases the performance of ensemble models, highlighting the importance of the use of market bids in electricity price forecasting.

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