Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia

AL‐Musaylh, Mohanad S.; Deo, Ravinesh C.; Adamowski, Jan; Li, Yan

doi:10.1016/j.rser.2019.109293

Cited by 57 publications

(21 citation statements)

References 67 publications

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“…Similarly, from Figure 8 , it can be seen that among the records of documents that reach HAP, the average MAPE value is lower in the frameworks that implement hybrid models of ML and multivariate dependency, such as those developed in [ 6 , 27 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. To verify the hypotheses of the differences in the means and variances in the MAPE, three hypothesis tests are carried out.…”

Section: Evaluation Of Model Accuracymentioning

confidence: 85%

“…The ANN models have been used in many studies for electric power forecasting [ 6 , 39 , 44 , 46 , 48 , 53 , 54 , 55 , 57 , 58 , 59 , 60 , 61 , 62 , 65 , 70 , 75 , 77 , 81 , 82 , 86 , 108 , 153 , 155 , 165 , 175 , 176 ] and have reached a forcasting accuracy with an average MAPE value of 3.781%.…”

Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

“…The load forecasts help to identify strategies to optimize the operating mechanisms in a determined period and thus ensure the demand even in situations adverse to the system [ 1 ]. Accompanying the rapid advances in forecasting theory [ 2 , 3 ] and machine learning [ 4 , 5 , 6 ], the technology in the energy forecasting research area has also developed rapidly [ 7 ]. Additionally, the popular prediction methods for the generation and demand of energy can be divided into two categories.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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Electric power forecasting plays a substantial role in the administration and balance of current power systems. For this reason, accurate predictions of service demands are needed to develop better programming for the generation and distribution of power and to reduce the risk of vulnerabilities in the integration of an electric power system. For the purposes of the current study, a systematic literature review was applied to identify the type of model that has the highest propensity to show precision in the context of electric power forecasting. The state-of-the-art model in accurate electric power forecasting was determined from the results reported in 257 accuracy tests from five geographic regions. Two classes of forecasting models were compared: classical statistical or mathematical (MSC) and machine learning (ML) models. Furthermore, the use of hybrid models that have made significant contributions to electric power forecasting is identified, and a case of study is applied to demonstrate its good performance when compared with traditional models. Among our main findings, we conclude that forecasting errors are minimized by reducing the time horizon, that ML models that consider various sources of exogenous variability tend to have better forecast accuracy, and finally, that the accuracy of the forecasting models has significantly increased over the last five years.

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Section: Evaluation Of Model Accuracymentioning

confidence: 85%

Section: Classes Of Forecasting Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Vivas

Allende‐Cid

Salas

2020

Entropy

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“…A detailed description of the ARIMA model can be found elsewhere and further applications of this method can be found in other's works [31,94,95]. Generally, the ARIMA model assumes a scenario where there is no change in consecutive periodical measurements or the readings used to construct a model.…”

Section: Computational Aspects Of Lstm Network Modelmentioning

confidence: 99%

“…To prepare the suitable number of inputs for each time-scale horizon (based on historical behaviour of short-term solar radiation measurements), the autocorrelation coefficient and the partial correlation coefficient (PACF) were employed. The detailed procedures can be found in [94]. Explicitly, the PACF function computes a time-series regression against its n-timescale lagged values by removing the dependency on intermediate elements and identifying those patterns potentially prevalent in the future GSR data that are correlated to the antecedent GSR data.…”

Section: Data Preparationmentioning

confidence: 99%

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

et al. 2020

Self Cite

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This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

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Gas consumption demand forecasting with empirical wavelet transform based machine learning model: A case study

AL‐Musaylh

Al‐Daffaie

Prasad

2021

Intl J of Energy Research

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Dispatchable, reliable, and clean energy is essential for sustained economic growth and a better future. This study develops a novel technique of empirical wavelet transform (EWT) to reveal intrinsic patterns in daily gas consumption demand data and ultimately forecast the daily gas consumption demand patterns for the city of Melbourne, Australia, via a hybrid decision tree (M5 model tree) model. EWT algorithm decomposes the explored dataset into the respective intrinsic mode functions (IMFs) and a residual component representing historical demand behavior overcoming non-linearity and non-stationary issues. These sub-series components reflect the frequency types, stochastic behaviors, and several other relevant patterns that are embedded into the model's input variable matrix representing historical changes in demand to forecast future gas consumption demand. An evaluation comparison was conducted between the hybrid EWT-M5 model tree algorithm, the hybrid EWT-multivariate adaptive regression spline (MARS) and the hybrid EWTautoregressive integrated moving average (ARIMA) models, including the traditional models based on M5 tree, MARS, and ARIMA algorithms. The results show that EWT-models improved significantly compared to traditionally adopted (ie, the non-EWT) methods. The performance metrics showed that the EWT-M5 model tree generated a RRMSE of 29.19% compared to 33.29% for EWT-MARS, 47.76% for EWT-ARIMA, 71.77% M5 model tree, 45.47% for MARS, and 65.25% for ARIMA. Accordingly, the results show the potential utility of EWT modelling approach in assisting the monitoring of energy usage and decision-making tasks for national energy markets.

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Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia

Cited by 57 publications

References 67 publications

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

A Systematic Review of Statistical and Machine Learning Methods for Electrical Power Forecasting with Reported MAPE Score

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Gas consumption demand forecasting with empirical wavelet transform based machine learning model: A case study

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