Combination Forecasting of Power Load Based on Polynomial Trend Extrapolation and ARIMA Model

2012 ◽  
Vol 546-547 ◽  
pp. 357-362 ◽  
Author(s):  
Jing Jing Xia ◽  
Huan Qi ◽  
Zi Qi Wang
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Arima Model ◽  
Forecast Accuracy ◽  
Electricity Consumption ◽  
Warning System ◽  
Polynomial Trend ◽  
Combination Forecasting ◽  
Power Load ◽  
Fuel Storage

The power load forecasting is the core component of the early warning system for fuel storage margin in power system and an important guarantee to the early warning function to achieve. In this paper, one province's 2008 load data is chosen to forecast the electricity consumption in 2009. Firstly the two forecasting models of polynomial trend extrapolation and ARIMA are established, and then the combined model of them is used to forecast, that is, the final result is equal to the sum of the trend value by polynomial extrapolation and the non-trend D-value’s forecasting result by ARIMA. The results indicate that the combination forecasting make the forecast accuracy significantly improved and ensure the effective operation of the early warning system.

Early-Warning System for Power Coal Storage and Supply Margin of Henan Province

2013 ◽  
Vol 291-294 ◽  
pp. 2366-2374
Author(s):  
Ye Fei Liu ◽  
Huan Qi ◽  
Su Qin Sun
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Power Plants ◽  
Thermal Power ◽  
Warning System ◽  
Electrical Energy ◽  
Henan Province ◽  
Electricity Production ◽  
Policy Makers ◽  
Fuel Storage

China's needs of energy increased dramatically in these years. In China, Electrical energy are mainly generated by thermal power plants that use coal as fuel, thus electricity supply are linked to the power fuel (coals) storage of power plants. Henan has been changed form an energy exporter province to an energy importer province. Therefore, the fuel storage and supply of power plants are keys to the security of the province's social development, economics and energy supply. Research the margin of power fuel storage and supply can help the policy makers to learn the security conditions and trends of electricity production microscopically, reducing the risks in the power production process, and improving the efficiency of production and the efficiency of energy. Environmental and economic issues brought by the excessive storage can be reduced. This article describes the ideas and development of early warning system for power fuel storage and supply margin of Henan province.

Early Warning System Design for WEEE Reverse Logistic Network

2019 ◽  
Vol 9 (4) ◽  
pp. 65-78 ◽  
Author(s):  
Bersam Bolat ◽  
Gül Temur ◽  
Dilay Çelebi ◽  
Berk Ayvaz ◽  
Ferhan Çebi
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Environmental Concern ◽  
Corporate Citizenship ◽  
Electricity Consumption ◽  
Warning System ◽  
Marmara Region ◽  
Ann Model ◽  
Factors Affecting ◽  
Target Values

The increase of environmental concern as a result of corporate citizenship spreads the applications for collecting end-of-life products to a broader extent. This trend raises the issue of reverse logistics (RL), one of the major challenges in sustainability. One of the greatest barriers for successful RL is the difficulty of developing an accurate system to forecast the amount of product returns. Advanced techniques such as learning systems are proven very helpful for increasing the performance of forecasting methods. This article proposes an “early warning system” for waste collection operations in the electrical and electronic equipment industry. The main goal is to develop a supportive system for manufacturers and authorized organizations that provides foresight about their potential to reach the target values proposed by environmental regulations. The proposed forecasting system is based on an artificial neural network (ANN) model with five basic factors affecting the amount of product return: sales amount, number of houses, electricity consumption, the GINI coefficient (coefficient showing income distribution inequality) and population density. An application of the system is shown for Marmara Region, Turkey, and the compliances of all the big cities in the Marmara Region are checked for target values. The researchers' findings show that only five of eleven cities will be successful at fulfilling the required target e-waste values addressed by WEEE regulations.

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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