The paper proposes use of multivariate adaptive regression splines (MARS) method to perform monthly electricity demand forecasting for medium-term. The model is developed based on specific example of Turkey; however is applicable to any other system. Performance of the proposed method is compared to that of multiple linear regression (MLR), generalized additive model (GAM), and artificial neural networks (ANN) methods. The validation process shows that the proposed model outperforms the other ones by test error and shows stable error performance
Forecasting is an essential function in the electricity supply industry. Electricity demand forecast...
The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is cruci...
Electricity load demand is the fundamental building block for all utilities planning. In recent year...
The paper presents a multivariate adaptive regression splines (MARS) modelling approach for daily pe...
Accurate prediction of daily peak load demand is very important for decision makers in the energy se...
Accurate and reliable forecasting models for electricity demand (G) are critical in engineering appl...
Electricity is one of the most important resources and fundamental infrastructure for every nation. ...
Reliable models that can forecast energy demand (G) are needed to implement affordable and sustainab...
This research focuses its efforts on the prediction of medium-term electricity consumption for scena...
This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict m...
Selection of appropriate climatic variables for prediction of electricity demand is critical as it a...
The growing effect of electricity prices on energy markets appeals for more accurate forecasting tec...
Medium-term forecasting is an important category of electric load forecasting that covers a time spa...
In this work we propose a new hybrid model, a combination of the manifold learning Principal Compone...
Kumru, Mesut (Dogus Author) -- Conference full title: International Conference on Industrial Enginee...
Forecasting is an essential function in the electricity supply industry. Electricity demand forecast...
The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is cruci...
Electricity load demand is the fundamental building block for all utilities planning. In recent year...
The paper presents a multivariate adaptive regression splines (MARS) modelling approach for daily pe...
Accurate prediction of daily peak load demand is very important for decision makers in the energy se...
Accurate and reliable forecasting models for electricity demand (G) are critical in engineering appl...
Electricity is one of the most important resources and fundamental infrastructure for every nation. ...
Reliable models that can forecast energy demand (G) are needed to implement affordable and sustainab...
This research focuses its efforts on the prediction of medium-term electricity consumption for scena...
This article provides a solution based on statistical methods (ARIMA, ETS, and Prophet) to predict m...
Selection of appropriate climatic variables for prediction of electricity demand is critical as it a...
The growing effect of electricity prices on energy markets appeals for more accurate forecasting tec...
Medium-term forecasting is an important category of electric load forecasting that covers a time spa...
In this work we propose a new hybrid model, a combination of the manifold learning Principal Compone...
Kumru, Mesut (Dogus Author) -- Conference full title: International Conference on Industrial Enginee...
Forecasting is an essential function in the electricity supply industry. Electricity demand forecast...
The ability to predict ggregated electricity demand of n electrical grid on an hourly basis is cruci...
Electricity load demand is the fundamental building block for all utilities planning. In recent year...