Levenberg-Marquardt Recurrent Networks for Long-Term Electricity Peak Load Forecasting

 Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2009-2011. Actual historical regional data which consists of economic, electricity statistic and weather during 1995-2008 are applied as inputs. The networks structure is firstly justified using true historical data of 1995-2005 to forecast peak load of 2006-2008. Afterwards, peak load forecasting of 2009-2011 is conducted subsequently using actual historical data of 1995-2008. Overall, the proposed networks shown better performance compared to that obtained by Levenberg-Marquardt-Feedforward network, Double-log Multiple Regression, and with projection by PLN for 2006-2010

Perancangan Sistem Microgrid Untuk Mempercepat Akses Terhadap Energi Listrik (Energy Access) Pada Kawasan Wisata Setu Rawalumbu Kota Bekasi

Setu Rawalumbu, Kecamatan Rawalumbu, Kota Bekasi, Propinsi Jawa Barat, Indonesia adalah satu dari ribuan setu yang ada di Indonesia, yang masih belum dimanfaatkan secara optimal, Permen PUPR No. 9 Tahun 2015 Tentang Penggunaan Sumber Daya Air pada pasal 43 ayat (2), dan Permen PUPR No. 28 Tahun 2015 Tentang Garis Sempadan Sungai dan Garis Sempadan Danau pada pasal 23. Ini bisa dilihat dari munculnya banyak perumahan di daerah tersebut termasuk fasilitas umum dan fasilitas sosial, dan menjadi  tempat pembuangan sampah yang berasal dari daerah sekitar.  Setu ini juga dicanangkan sebagai Daerah Kawasan Wisata yang fokusnya pada eko-wisata (eco-tourism), yang akan memberikan manfaat ekonomi kepada masyarakat setempat dan mendukung upaya pelestarian lingkungan alam dan budaya. Sebagai Daerah Kawasan Wisata, energi listrik yang berkelanjutan di Setu ini perlu dioptimalkan sesuai dengan kriteria goal ke 7 dari Sustainable Development Goals (SDGs), dengan membangun microgrid energi terbarukan dengan back-up generator diesel dan memanfaatkan potensi sumberdaya matahari. Energi listrik yang dibangkit dari sinar matahari melalui solar PV adalah energi yang bersih atau hijau (clean energy atau green energy). Sistem ini lebih andal, murah dan berdampak lingkungan rendah. Dengan cara ini, Daerah Kawasan Wisata Setu Rawalumbu dapat menjadi daerah eko-wisata (eco-tourism).   Kata Kunci: Setu; Rawalumbu; EnergiSetu Rawalumbu, Rawalumbu Sub-District, Bekasi City, West Java Province, Indonesia is one of thousands of Ponds in Indonesia, which is still not optimally utilized, PUPR Regulation No. 9 of 2015 concerning Use of Water Resources in article 43 paragraph (2), and PUPR Minister Regulation No. 28 of 2015 concerning River Borderline and Lake Borderline in article 23. This can be seen from the emergence of a lot of housing in the area including public and social facilities, and becoming a landfill that comes from the surrounding area. This pond is also declared as a Regional Tourism Area which focuses on eco-tourism, which will provide economic benefits to the local community and support efforts to preserve the natural and cultural environment. As a Tourism Area Region, sustainable electricity in the pond needs to be optimized according to the 7th goal criteria of Sustainable Development Goals (SDGs), by building renewable energy microgrids with back-up diesel generators and utilizing the potential of solar resources. Electrical energy that is generated from sunlight through solar PV is clean or green energy. This system is more reliable, inexpensive and has a low environmental impact. In this way, the Setu Rawalumbu Regional Tourism Area can become an eco-tourism area. Keywords: Setu; Rawalumbu; Renewable Energy

Levenberg-Marquardt Recurrent Networks for Long- Term Electricity Peak Load Forecasting

Increasing electricity demand in Java-Madura-Bali, Indonesia, must be addressed appropriately to avoid blackout by determining accurate peak load forecasting. Econometric approach may not be sufficient to handle this problem due to limitation in modelling nonlinear interaction of factors involved. To overcome this problem, Elman and Jordan Recurrent Neural Network based on Levenberg-Marquardt learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection for 2009-2011. Actual historical regional data which consists of economic, electricity statistic and weather during 1995-2008 are applied as inputs. The networks structure is firstly justified using true historical data of 1995-2005 to forecast peak load of 2006-2008. Afterwards, peak load forecasting of 2009-2011 is conducted subsequently using actual historical data of 1995-2008. Overall, the proposed networks shown better performance compared to that obtained by Levenberg-Marquardt-Feedforward network, Double-log Multiple Regression, and with projection by PLN for 2006-2010

Long-term Peak Load Forecasting Using LMFeedforward Neural Network for Java-Madura-Bali Interconnection, Indonesia

This paper presents the application of artificial neural network (ANN) based on multi-layered feedforward backpropagation for long-term peak load forecasting (LTPF). A four-layered network using Levenberg-Marquardt (LM) learning algorithm is proposed to forecast annual peak load of Java-Madura-Bali interconnection, Indonesia, for the period of 2009-2018 considering 11 regional factors encompass economic, electricity statistics, and weather thought to affect the load demand. The proposed network structure is first trained over the past 11 years (1995-2005) to forecast annual peak load of 2006-2008. Afterwards, the justified network structure is trained over the past 14 years (1995-2008) to forecast annual peak load of 2009-2018. Several simulations involve changes in historical actual peak load target and variation on projected regional economic growth are carried out to observe the network adaptability. Results are then compared with that achieved by the multiple regression model and projection made by utility. In this case, forecasting result exhibited by the proposed network is the closest to actual values of 2006-2009 among others taken the average error of 0.2%. Likewise, its forecasting differences for 2010-2018 are less than 7% compared to others. In term of network adaptability, outputs generated by the network are well adjusted to the projected inputs variation

Environmental and Utility Planning Implications Assessment of Sulfur Tax : The case of fndonesian Power Sector.

This paper presents the environmental and utility planning implications assessment of sulfur tax es an alternative instrument for SOt emission reduction from the Indonesian power sector. The implications were analyzed based on a long-term Traditional Resource Planning perspective. The methodologr used to calculate the implication is least cost expansion model expanded by Integrated Resource Planning Model less Demand Side Management @SfvD option. SevLn scenarios based on sulfur tax rate have been selected. The planning horizon period is 2006-2025. The environmental implication shows that SO2 emission would decrease significantly i.e. 40o/o at sulfur tax rate of US$250/tS end 847o at sulfur tax rate of US$0OltS, while at the same rate CO2 and NO, emissions would decrease to 537o and 677o respectively. From generation system sspect, introducing sulfur tax to power sector would promote the selection of clean technologr power plant for expansion planning. The generation plant mix would reduce the consumption of coal fuel and increase the consumption of gas

Environmental and Utility Planning Implications Assessment of Sulfur Tax : The case of fndonesian Power Sector.

This paper presents the environmental and utility planning implications assessment of sulfur tax es an alternative instrument for SOt emission reduction from the Indonesian power sector. The implications were analyzed based on a long-term Traditional Resource Planning perspective. The methodologr used to calculate the implication is least cost expansion model expanded by Integrated Resource Planning Model less Demand Side Management @SfvD option. SevLn scenarios based on sulfur tax rate have been selected. The planning horizon period is 2006-2025. The environmental implication shows that SO2 emission would decrease significantly i.e. 40o/o at sulfur tax rate of US$250/tS end 847o at sulfur tax rate of US$0OltS, while at the same rate CO2 and NO, emissions would decrease to 537o and 677o respectively. From generation system sspect, introducing sulfur tax to power sector would promote the selection of clean technologr power plant for expansion planning. The generation plant mix would reduce the consumption of coal fuel and increase the consumption of gas