Development of Micro-Spatial Electricity Load Forecasting Methodology Using Multivariate Analysis for Dynamic Area in Tangerang, Indonesia

Dynamic population and land use significantly affect future energy demand. This paper proposes a suitable method to forecast load growth in a dynamic area in Tangerang, Indonesia. This research developed micro-spatial load forecasting, which can show load centres in microgrids, estimate the capacity and locate the distribution station precisely. Homogenous grouping implemented the method into clusters consisted of microgrids. It involves multivariate variables containing 12 electric and non-electric variables. Multivariate analysis is conducted by carrying out Principal Component Analysis (PCA) and Factor Analysis. The forecasting results can predict load growth, time, and location, which can later be implemented as the basis of a master electricity distribution plan because it provides an accurate long-term forecast.

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Micro-Spatial Electricity Load Forecasting Clustering Technique

E3S Web of Conferences ◽

10.1051/e3sconf/202020211005 ◽

2020 ◽

Vol 202 ◽

pp. 11005

Author(s):

Christine Widyastuti ◽

Adri Senen ◽

Oktaria Handayani

Keyword(s):

Load Forecasting ◽

Similar Data ◽

Load Forecast ◽

Load Growth ◽

Investment Cost ◽

Accurate Forecast ◽

Distribution Station ◽

Electricity Load ◽

Micro Grids ◽

Data Group

Low growth of electricity load forecast eliminates cost opportunity of electricity sale due to unserviceable load demands. Meanwhile, if it is exorbitant, it will cause over-investment and incriminate investment cost. Existing method of sector load is simplified and easy to implement. However, the accuracy tends to bias over one area of which data is limited and dynamic service area. Besides, the results of its forecast is macro-based, which means it is unable to show load centres in micro grids and failed to locate the distribution station. Therefore, we need micro-spatial load forecasting. By using micro-spatial load forecast, the extrapolated areas are grouped into grids. Clustering analysis is used for grouping the grids. It generates similarity matrix of similar data group. Clustering involves factors causing load growth at each grid; geography, demography, socio-economic, and electricity load per sector. Results of every cluster consist of different regional characteristics, which later the load growth is projected as to obtain more accurate forecast..

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An adaptive backpropagation algorithm for long-term electricity load forecasting

Neural Computing and Applications ◽

10.1007/s00521-021-06384-x ◽

2021 ◽

Author(s):

Nooriya A. Mohammed ◽

Ammar Al-Bazi

Keyword(s):

Load Forecasting ◽

Backpropagation Algorithm ◽

Electricity Load ◽

Electricity Load Forecasting

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Mid-long Term Load Forecasting Model Based on Principal Component Regression

Lecture Notes in Electrical Engineering - Frontier Computing ◽

10.1007/978-981-13-3648-5_204 ◽

2019 ◽

pp. 1577-1583

Author(s):

Xia Xinmao ◽

Zhang Zengqiang ◽

Yuan Chenghao ◽

Yu Zhiyong

Keyword(s):

Load Forecasting ◽

Principal Component Regression ◽

Principal Component ◽

Forecasting Model ◽

Model Based

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Short- and long-term electricity load forecasting using classical and neural network based approach: A case study for the Philippines

2016 IEEE Region 10 Conference (TENCON) ◽

10.1109/tencon.2016.7848779 ◽

2016 ◽

Author(s):

Mary Joyce T. Bantugon ◽

Russel John C. Gallano

Keyword(s):

Neural Network ◽

Load Forecasting ◽

The Philippines ◽

Short And Long Term ◽

Electricity Load ◽

Electricity Load Forecasting

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A new approach for long-term electricity load forecasting

2013 8th International Conference on Electrical and Electronics Engineering (ELECO) ◽

10.1109/eleco.2013.6713816 ◽

2013 ◽

Cited By ~ 2

Author(s):

Amir Safdarian ◽

Mahmud Fotuhi-Firuzabad ◽

Matti Lehtonen ◽

Milad Aghazadeh ◽

Aydogan Ozdemir

Keyword(s):

Load Forecasting ◽

New Approach ◽

Electricity Load ◽

Electricity Load Forecasting

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Long-Term Electricity Load Forecasting Considering Volatility Using Multiplicative Error Model

Energies ◽

10.3390/en11123308 ◽

2018 ◽

Vol 11 (12) ◽

pp. 3308 ◽

Cited By ~ 3

Author(s):

Swasti Khuntia ◽

Jose Rueda ◽

Mart van der Meijden

Keyword(s):

Load Forecasting ◽

Error Model ◽

Economic Research ◽

Arch Model ◽

Multiplicative Error ◽

Directional Accuracy ◽

Out Of Sample ◽

Electricity Load ◽

Electricity Load Forecasting

Long-term electricity load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment on the construction of excess power facilities, while an underestimate of the future load will result in insufficient generation and inadequate demand. As a first of its kind, this research proposes the use of a multiplicative error model (MEM) in forecasting electricity load for the long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, as accessed from a United States (U.S.) regional transmission operator, and recession data, accessed from the National Bureau of Economic Research, are used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. Historical volatility is used to account for implied volatility. To incorporate future volatility, backtesting of MEM is performed. Two performance indicators used to assess the proposed model are: (i) loss functions in terms of mean absolute percentage error and mean squared error (for both in-sample model fit and out-of-sample forecasts) and (ii) directional accuracy.

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