Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

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

doi:10.1016/j.aei.2017.11.002

Cited by 233 publications

(94 citation statements)

References 68 publications

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“…The results showed that the ARIMA model performed better than ARMA at forecasting longer periods of time, while ARMA is better at shorter periods of time. The ARIMA methods were applied in [21] by Mohanad et al to predict short-term electricity demand in Queensland (Australia) market. ARMA is usually applied on stationary stochastic processes [6] while ARIMA on non-stationary cases [22].…”

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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Section: Related Workmentioning

confidence: 99%

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

et al. 2018

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“…For energy, Edigera et al [65] used ARIMA to forecast Turkey's primary energy consumption and found that the ARIMA forecasting of the total primary energy demand appears to be more reliable than the summation of the individual forecasts. Musaylh et al [66] used ARIMA to forecast short-term electricity demand in Australia. Jiang [67] take advantage of ARIMA to calculate China's coal consumption and price from 2016 to 2030.…”

Section: Review Of Arima Modelmentioning

confidence: 99%

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Han

2019

Energies

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Forecasting energy demand is the basis for sustainable energy development. In recent years, the new discovery of East Africa’s energy has completely reversed the energy shortage, having turned the attention of the world to the East African region. Systematic research on energy forecasting in Africa, particularly in East Africa, is still relatively rare. In view of this, this study uses a variety of methods to comprehensively predict energy consumption in East Africa. Based on the traditional grey model, this study: (1) Integrated the power coefficient and metabolic principles, and then proposed non-linear metabolic grey model (NMGM) forecasting model; (2) Used Auto Regressive Integrated Moving Average Model (ARIMA) for secondary modeling, and then developed a metabolic grey model-Auto Regressive Integrated Moving Average Model (MGM-ARIMA) and non-linear metabolic grey model-Auto Regressive Integrated Moving Average Model (NMGM-ARIMA) combined models. In terms of the prediction interval, the data for 2000–2017 is a fit to the past stage, while the data for 2018–2030 is used for the prediction of the future stage. To measure the effect of the prediction, the study used the average relative error indicator to evaluate the accuracy of different models. The results indicate that: (1) Mean relative errors of NMGM, MGM-ARIMA, and NMGM-ARIMA are 2.9697%, 2.0969%, and 1.4654%, proving that each prediction model is accurate; (2) Compared with the single model, the combined model has higher precision, confirming the superiority and feasibility of model combination. After prediction, the conclusion shows that East Africa’s primary energy consumption will grow by about 4 percent between 2018 and 2030. In addition, the limitation of this study is that only single variable are considered.

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“…Several studies were conducted for short or/and longterm electricity demand projection that can be categorized into six types: regression based [29], autoregressive integrated moving average (ARIMA) [30], artificial neural networks [31], fuzzy logic [32], support vector [33], and system dynamics models [34]. The system dynamics approach is able to handle the dynamic evolution of vital energy forecasting variables with feedback loops among each other [35] and allows the incorporation of stochastic behavior [36].…”

Section: Electricity Demand Modelmentioning

confidence: 99%

Supply optimization based on society’s cost of electricity and a calibrated demand model for future renewable energy transition in Niger

Tilahun¹,

Bhandari²,

Mamo³

2019

Energ Sustain Soc

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Background:In an attempt to realize the most beneficial and optimal mix of electricity generation in Niger, a society's cost of electricity (SCOE) as the levelized cost of electricity (LCOE) plus the cost of climate change and air pollution is formulated. The SCOE is used as a basis for setting the performance criteria for supply optimization to balance future electricity demand in Niger. The demand projection is derived from a system dynamics model that anticipates future changes based on its influencing factors of population growth, urbanization progress, and industrial development. Results:Reference case and what-if scenarios are simulated to see the projected electricity demand and possible renewable energy transition pathways. As a result, for example, the annual electricity of 3214 GWh estimated for 2030 might be balanced with about 200 MW, 104 MW, 85 MW, and 63 MW power from hydropower, grid-tied PV, coal, and diesel for SCOE optimized grid. For the same case, a greenhouse gas (GHG) emission reduction of 11.26-15.66% could be achieved when compared to the LCOE-based optimized grid. In addition, the most preferred energy option hydropower will be almost exhausted when the projected electricity demand in 2035 reaches slightly more than double its value in 2025. Thus, Niger should start exploiting its solar as well as conventional sources intensively to balance its future energy demand. Conclusions: In this work, a mixed energy grid is optimized primarily on affordability while considering its sustainability. The implemented holistic approach lessens the need for energy import in the country and provides relief to energy security issues such as electricity price volatility and supply reliability. Additionally, the proposed strategy helps to guide the renewable energy transition pathway in Niger.

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Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Cited by 233 publications

References 68 publications

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting

Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model

Supply optimization based on society’s cost of electricity and a calibrated demand model for future renewable energy transition in Niger

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