Medium-Term Electricity Demand Forecasting Based on MARS

Date

2017-09-29

Author

İlseven, Engin
Göl, Murat

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

224
views

0
downloads

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.

Subject Keywords

Multiple linear regression, Generalized additive models, Multivariate adaptive regression splines, Artificial neural networks, Electricity demand forecasting

URI

https://hdl.handle.net/11511/55240

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey Tamer, Tolga; Gürsel Dino, İpek; Akgül, Çağla (2022-07-01) This research presents a methodological framework for lifetime energy demand and PV energy generation predictions for a given building considering the CC impacts through multivariate regression models. As a case study, a hypothetical office building in Turkey was selected. An existing linear morphing methodology was utilized to generate future weather files for all 81 cities in Turkey. For each year and city, corresponding weather metrics were calculated, and heating/cooling demand and PV energy generation ...
Hydro-Optimization-Based Medium-Term Price Forecasting Considering Demand and Supply Uncertainty İLSEVEN, Engin; Göl, Murat (2018-07-01) This paper proposes an electricity market model of Turkish electricity market for monthly and yearly electricity price forecasting in medium-term by means of supply and demand dynamics formed via a theoretical approach. The electricity market model created within this scope consists of three main components related to electricity demand, supply, and price segments along with hydro optimization submodel, which takes into account the nonlinear relation between supply and price. Electricity price is determined...
Assessment of solar data estimation models for four cities in Iran Jahani, Elham; Sadati, S. M. Sajed; Yousefzadeh, Moslem (2015-04-29) The estimated solar resources are important for designing renewable energy systems since measured data are not always available. The estimation models have been introduced in several studies. These models are mainly dependent on local meteorological data and need to be assessed for different locations and times. The current study compares the results of Angstrom's model and a neural network (NN) model developed for this study with measured data for four cities in Iran. The time resolution for the estimated ...
Solar Power Generation Analysis and Forecasting Real-World Data Using LSTM and Autoregressive CNN tosun, nail; sert, egemen; Ayaz, Enes; YILMAZ, ekin; GÖL, MURAT (2020-09-22) Generated power of a solar panel is volatile and susceptible to environmental conditions. In this study, we have analyzed variables affecting the generated power of a 17.5 kW real-world solar power plant with respect to five independent variables over the generated power: irradiance, time of measurement, panel's temperature, ambient temperature and cloudiness of the weather at the time of measurement. After our analysis, we have trained three different models to predict intra-day solar power forecasts of th...
Efficient adaptive regression spline algorithms based on mapping approach with a case study on finance Koc, Elcin Kartal; İyigün, Cem; Batmaz, İnci; Weber, Gerhard-Wilhelm (2014-09-01) Multivariate adaptive regression splines (MARS) has become a popular data mining (DM) tool due to its flexible model building strategy for high dimensional data. Compared to well-known others, it performs better in many areas such as finance, informatics, technology and science. Many studies have been conducted on improving its performance. For this purpose, an alternative backward stepwise algorithm is proposed through Conic-MARS (CMARS) method which uses a penalized residual sum of squares for MARS as a T...

Citation Formats

E. İlseven and M. Göl, “Medium-Term Electricity Demand Forecasting Based on MARS,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55240.