Development of data intelligent models for electricity demand forecasting: case studies in the state of Queensland, Australia

PhD Thesis


Al-Musaylh, Mohanad Shakir Khalid. 2020. Development of data intelligent models for electricity demand forecasting: case studies in the state of Queensland, Australia. PhD Thesis Doctor of Philosophy. University of Southern Queensland. https://doi.org/10.26192/z7tb-4754
Title

Development of data intelligent models for electricity demand forecasting: case studies in the state of Queensland, Australia

TypePhD Thesis
Authors
AuthorAl-Musaylh, Mohanad Shakir Khalid
SupervisorDeo, Ravinesh C.
Li, Yan
Adamowski, Jan
Institution of OriginUniversity of Southern Queensland
Qualification NameDoctor of Philosophy
Number of Pages138
Year2020
Digital Object Identifier (DOI)https://doi.org/10.26192/z7tb-4754
Abstract

Electricity demand (G) forecasting is a sustainability management and evaluation task for all energy industries, required to implement effective energy security measures and determine forward planning processes in electricity production and management of consumer demands. Predictive models for G forecasting are utilized as scientific stratagems for such decision-making. The information generated from forecast models can be used to provide the right decisions regarding the operation of National Electricity Markets (NEMs) through a more sustainable electricity pricing system, energy policy, and an evaluation of the feasibility of future energy distribution networks. Data intelligent models are considered as potential forecasting tools, although challenges related to issues of non-stationarity, periodicity, trends, stochastic behaviours in G data and selecting the most relevant model inputs remain a key challenge.

This doctoral thesis presents a novel study on the development of G forecasting models implemented at multiple lead-time forecast horizons utilizing data-intelligent techniques. The study develops predictive models using real G data from Queensland (second largest State in Australia) where the electricity demand continues to elevate. This research is therefore, divided into four primary objectives designed to produce a G forecasting system with data-intelligent models.

In first objective, the development and evaluation of a multivariate adaptive regression splines (MARS), support vector regression (SVR) and autoregressive integrated moving average (ARIMA) model was presented for short-term (30 minutes, hourly and daily) forecasting using Queensland’s aggregated G data. MARS outperformed SVR and ARIMA models at 30-minute and hourly horizon, while SVR was the best model for daily G forecasting.

The second objective reported the successful design of SVR model for daily period, including short-term periods (e.g., weekends, working days, and public holidays), and the long-term (monthly) period. Subsequently, the hybrid SVR, with particle swarm optimization (i.e., PSO-SVR) integrated with improved empirical mode decomposition with adaptive noise (ICEEMDAN) tool was constructed where PSO is adopted to optimize SVR parameters and ICEEMDAN was adopted to address non-linearity and non-stationary in G data. The capability of ICEEMDAN-PSO-SVR to forecast G was benchmarked against ICEEMDAN-MARS and ICEEMDAN-M5 Tree, including traditional PSO-SVR, MARS and M5 model tree methods.

As G is subjected to the influence of exogenous factors (e.g., climate variables), the third objective established a G forecasting model utilizing atmospheric inputs from the Scientific Information for Land Owners (SILO) observed data fields and the European Centre for Medium Range Weather Forecasting outputs. These models were developed using G extracted from the Energex database for eight stations in southeast Queensland for an artificial neural network (ANN) model over 6-hourly and daily forecast horizons.

The final objective was to advance the methods in previous objectives, by applying wavelet transformation (WT) as a decomposition tool to model daily G. Using real data from the University of Sothern Queensland (Toowoomba, Ipswich, and Springfield), the maximum overlap discrete wavelet transform (MODWT) was adopted to construct the MODWT-PACF-online sequential extreme learning machine (OS-ELM) model. The results revealed that newly developed MODWT-PACF-OSELM (MPOE) model attained superior performance compared to the models without the WT algorithm.

In synopsis, the predictive models developed in this doctoral thesis will to provide significant benefits to National Electricity Markets in respect to energy distribution and security, through new and improved energy demand forecasting tools. Energy forecasters can therefore adopt these novel methods, to address the issues of nonlinearity and non-stationary in energy usage whilst constructing a real-time forecasting system tailored for energy industries, consumers, governments and other stakeholders.

Keywordsenergy security, time­Series forecasting, predictive model for electricity demand, machine learning, artificial intelligence, operations research
ANZSRC Field of Research 2020370201. Climate change processes
490501. Applied statistics
490108. Operations research
490304. Optimisation
461199. Machine learning not elsewhere classified
Byline AffiliationsSchool of Sciences
Permalink -

https://research.usq.edu.au/item/q5wx3/development-of-data-intelligent-models-for-electricity-demand-forecasting-case-studies-in-the-state-of-queensland-australia

Download files


Published Version
  • 247
    total views
  • 240
    total downloads
  • 1
    views this month
  • 1
    downloads this month

Export as

Related outputs

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network
Deo, Ravinesh C., Grant, Richard H., Webb, Ann, Ghimire, Sujan, Igoe, Damien P., Downs, Nathan J., Al-Musaylh, Mohanad S., Parisi, Alfio V. and Soar, Jeffrey. 2022. "Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network." Stochastic Environmental Research and Risk Assessment. 36, p. 3183–3220. https://doi.org/10.1007/s00477-022-02188-0
Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results
Ghimire, Sujan, Deo, Ravinesh C., Wang, Hua, Al-Musaylh, Mohanad S., Casillas-Perez, David and Salcedo-sanz, Sancho. 2022. "Stacked LSTM Sequence-to-Sequence Autoencoder with Feature Selection for Daily Solar Radiation Prediction: A Review and New Modeling Results." Energies. 15 (3), pp. 1-39. https://doi.org/10.3390/en15031061
Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms
Al-Musaylh, Mohanad S., Deo, Ravinesh C. and Li, Yan. 2020. "Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms." Energies. 13 (9). https://doi.org/10.3390/en13092307
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 F. and Li, Yan. 2019. "Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia." Renewable and Sustainable Energy Reviews. 113. https://doi.org/10.1016/j.rser.2019.109293
Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset
Al-Musaylh, Mohanad S., Deo, Ravinesh C. and Li, Yan. 2018. "Particle swarm optimized–support vector regression hybrid model for daily horizon electricity demand forecasting using climate dataset." Huang, Q. and Kolhe, M. (ed.) 3rd International Conference on Power and Renewable Energy (ICPRE 2018). Berlin, Germany 21 - 24 Sep 2018 Germany. https://doi.org/10.1051/e3sconf/20186408001
Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting
Al-Musaylh, Mohanad S., Deo, Ravinesh C., Li, Yan and Adamowski, Jan F.. 2018. "Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting." Applied Energy. 217, pp. 422-439. https://doi.org/10.1016/j.apenergy.2018.02.140
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 F. and Li, Yan. 2018. "Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia." Advanced Engineering Informatics: the science of supporting knowledge-intensive activities. 35 (C), pp. 1-16. https://doi.org/10.1016/j.aei.2017.11.002