Modeling and forecasting electric daily peak loads using abductive networks

Abdel-Aal, R.E.

doi:10.1016/j.ijepes.2005.11.006

Cited by 33 publications

(5 citation statements)

References 28 publications

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“…Huang and Shih utilized a combination of fuzzy modeling and GMDH networks on Taiwan's electric load data in order to improve the performance of their short-term load forecast model against ANN and ARIMA [27]. Abdel-Aal employed GMDH networks on Seattle's electrical and weather data to obtain analytical expressions between input and output variables in forecasting hourly and daily electric loads with different variations of ANN, abductive networks, and network committees (NC) [28][29][30]. Elattar et al proposed a generalized locally weighted GMDH networks based EA for short-term load forecasting and performed the algorithm along with local support vector regression (LSVR), locally weighted GMDH networks (LWGMDH), locally weighted support vector regression (LWSVR), and traditional GMDH networks on two different data sets belonging to New York City and Victorian electricity market of Australia [31].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

et al. 2020

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Over the past decade, energy forecasting applications not only on the grid side of electric power systems but also on the customer side for load and demand prediction purposes have become ubiquitous after the advancements in the smart grid technologies. Within this context, short-term electrical energy consumption forecasting is a requisite for energy management and planning of all buildings from households and residences in the small-scale to huge building complexes in the large-scale. Today’s popular machine learning algorithms in the literature are commonly used to forecast short-term building electrical energy consumption by generating an abstruse analytical expression between explanatory variables and response variables. In this study, gene expression programming (GEP) and group method of data handling (GMDH) networks are meticulously employed for creating genuine and easily understandable mathematical models among predictor variables and target variables and forecasting short-term electrical energy consumption, belonging to a large hospital complex situated in the Eastern Mediterranean. Consequently, acquired results yielded mean absolute percentage errors of 0.620% for GMDH networks and 0.641% for GEP models, which reveal that the forecasting process can be accomplished and formulated simultaneously via proposed algorithms without the need of applying feature selection methods.

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Section: Related Workmentioning

confidence: 99%

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

et al. 2020

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“…79/1999 for the implementation of the free market. It is an organized system that is able to promote both competition relating to the production and sale of electricity, and the protection of end customers through a maximum transparency ensured through the coordination of the unified electricity market entrusted to the Electricity Market Operator (GME) 1 . As regarding the market mechanisms, the MPE is divided into Day-Ahead Market (MGP), Intra-Day Market (MI), Services Market (MSD).…”

Section: The Italian Contextmentioning

confidence: 99%

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Harasheh

2016

Preprint

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Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of energy sector worldwide late 1990s. Given these facts, this paper tries to come up with a precise and flexible forecasting model for the wholesale electricity price for the Italian power market on an hourly basis. We utilize artificial intelligence models such as neural networks and bagged regression trees that are rarely used to forecast electricity prices. After model calibration, our final model is bagged regression trees with exogenous variables. The selected model outperformed neural network and bagged regression with single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm some theoretical specifications of the model. As a policy implication, this model might be used by energy traders, transmission system operators and energy regulators for an enhanced decisionmaking process.JEL Codes: C14, C45, C53, Q47.

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“…Additionalclassifications such as optimization models, equilibrium models, agent-based models, and artificial intelligence models or game theory models, simulation models and the time-series models as in (Aggarwal et al, 2009) can also be used. Table (1) summarizes the EPF models as in (Weron, 2014), however, we add the last type in computational intelligence models (bootstrap aggregation of regression trees). (Weron, 2014) The groups of models are summarized as follows as in (Weron, 2014):…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

Harasheh

2016

ESJ

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Electricity price forecasting has become a crucial element for both private and public decision-making. This importance has been growing since the wave of deregulation and liberalization of the energy sector on a global scale since the late 1990s. Given these facts, this paper is an attempt to establish and demonstrate a precision based applicable forecasting model for wholesale electricity prices with respect to the Italian power market on an hourly basis. Artificial intelligence models such as neural networks and bagged regression trees are utilized, although they are rarely used to forecast electricity prices. After model calibration, bagged regression trees with exogenous variables comprised the final model. The selected model outperformed neural network and bagged regression with a single price used in this paper, it also outperformed other statistical and non-statistical models used in other studies. We also confirm certain theoretical specifications of the model. As a policy tool, this model could be used by energy traders, transmission system operators and energy regulators for an enhanced decision-making process.

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Modeling and forecasting electric daily peak loads using abductive networks

Cited by 33 publications

References 28 publications

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks

Forecasting Wholesale Electricity Prices With Artificial Intelligence Models: The Italian Case

Forecasting The Italian Day-Ahead Electricity Price Using Bootstrap Aggregation Method

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