scholarly journals Influence of Climate and Nonclimate Parameters on Monthly Electricity Consumption in Niger

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Abdou Latif Bonkaney
Keyword(s):  
Electricity Consumption ◽  
Small Error ◽  
Heat Index ◽  
Percentage Error ◽  
Clearness Index ◽  
Heat Indices ◽  
Linear Regressions ◽  
Monthly Electricity Consumption ◽  
Relevant Factors ◽  
Better Than

This study examines the impacts of relevant factors (climatic and nonclimatic) on the monthly electricity consumption (MEC) in four major cities in Niger using simple multiple linear regressions (MLRs). Parameters such GDP/capita, air temperature (Tmean), relative humidity (RH), wind speed (WSP), solar radiation (SR), precipitation, and clearness index (K) are used. In addition, two heat indices, heat index (HI) and discomfort index (DI) are calculated to take into account the impacts of high humidity in conjunction with high ambient temperature. Hence, three different models were derived from the aforementioned variables. The three models have been tested using the k-folds cross-validation. Results show that the model with primitive variables such GDP per capita, Tmean, RH, SR, and WSP perform better than the other two models with a coefficient determination R2 equal to 0.87, 0.854, 0.833, and 0.551 for Niamey, Maradi, Zinder, and Agadez, respectively. According to the month considered, the mean absolute percentage error can give a small error for specific combinations of climate variables. The variables such as precipitation and clearness index are found to be not statistically significant.

scholarly journals Forecasting Electricity Consumption Using Time Series Model

2018 ◽  
Vol 7 (4.30) ◽  
pp. 218 ◽  
Author(s):  
Y.W. Lee ◽  
K.G. Tay ◽  
Y.Y. Choy
Keyword(s):  
Mean Square Error ◽  
Future Development ◽  
Linear Trend ◽  
Moving Average ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Electricity Sector ◽  
Percentage Error ◽  
Mean Square ◽  
Monthly Electricity Consumption

Electricity demand forecasting is important for planning and facility expansion in the electricity sector.  Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development.  Universiti Tun Hussein Onn Malaysia (UTHM) which is a developing university in Malaysia has been growing since its formation in 1993.  Thus, it is important for UTHM to forecast the electricity consumption in future so that the future development can be determined.  Hence, UTHM electricity consumption was forecasted by using the simple moving average (SMA), weighted moving average (WMA), simple exponential smoothing (SES), Holt linear trend (HL), Holt-Winters (HW) and centered moving average (CMA).  The monthly electricity consumption from January 2011 to December 2017 was used to forecast January to December 2018 monthly electricity consumption.  HW gives the smallest mean absolute error (MAE) and mean absolute percentage error (MAPE), while CMA produces the lowest mean square error (MSE) and root mean square error (RMSE).  As there is a decreasing population of UTHM after the moving of four faculties to Pagoh and HW forecasted trend is decreasing whereas CMA is increasing, hence HW might forecast better in this problem.

scholarly journals PERAMALAN JUMLAH PENUMPANG PESAWAT BANDARA I GUSTI NGURAH RAI MENGGUNAKAN EXPONENTIAL SMOOTHING DAN RUEY-CHYN TSAUR

2021 ◽  
Vol 10 (4) ◽  
pp. 222
Author(s):  
WILDAN FATTURAHMAN MUJTABA ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Small Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Forecast Method ◽  
Tourist Destination ◽  
Forecasting Methods ◽  
Airline Passengers ◽  
Better Than

Bali province is a tourist destination island with good transportation. Airplane is the most used transportation to go to Bali. Convenience of the airline passengers are the most important thing for I Gusti Ngurah Rai Airport Authorithy. An exact forecast method is needed to predict the numbers of passenger in the future. There are two types of forecasting methods; triple exponential smoothing and Fuzzy Time Series Ruey-Chyn Tsaur, however based on the research Fuzzy Time Series Ruey-Chyn Tsaur is better than triple exponential smoothing due to a small error MAPE (Mean Absolute Percentage Error) of 2,4% and plot is close to actual data.

scholarly journals COMPARISON OF LINEAR REGRESSIONS AND NEURAL NETWORKS FOR FORECASTING ELECTRICITY CONSUMPTION

2020 ◽  
Vol 16 (2) ◽  
pp. 135-140
Author(s):  
Tyas Setiyorini ◽  
Frieyadie Frieyadie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Linear Regression ◽  
Electricity Consumption ◽  
Nonlinear Problems ◽  
Complex Data ◽  
The Neural Network ◽  
The Neural Networks ◽  
Linear Regressions ◽  
Better Than

Electricity has a major role in humans that is very necessary for daily life. Forecasting of electricity consumption can guide the government's strategy for the use and development of energy in the future. But the complex and non-linear electricity consumption dataset is a challenge. Traditional time series models in such as linear regression are unable to solve nonlinear and complex data-related problems. While neural networks can overcome the problems of nonlinear and complex data relationships. This was proven in the experiments in this study. Experiments carried out with linear regressions and neural networks on the electricity consumption dataset A and the electricity consumption dataset B. Then the RMSE results are compared on the linear regressions and neural networks of the two datasets. On the electricity consumption dataset, A obtained by RMSE of 0.032 used the linear regression, and RMSE of 0.015 used the neural network. On the electricity consumption, dataset B obtained by RMSE of 0.488 used the linear regression, and RMSE of 0.466 used the neural network. The use of neural networks shows a smaller RMSE value compared to the use of linear regressions. This shows that neural networks can overcome nonlinear problems in the electricity consumption dataset A and the electricity consumption dataset B. So that the neural networks are afforded to improve performance better than linear regressions.  This study to prove that there is a nonlinear relationship in the electricity consumption dataset used in this study, and compare which performance is better between using linear regression and neural networks.

Monthly Electricity Consumption Forecasting: A Step-Reduction Strategy and Autoencoder Neural Network

2020 ◽  
pp. XX10-XX10
Author(s):  
Zhenghui Li ◽  
Kangping Li ◽  
Fei Wang ◽  
Zhiming Xuan ◽  
Zengqiang Mi ◽  
...  
Keyword(s):  
Neural Network ◽  
Electricity Consumption ◽  
Reduction Strategy ◽  
Monthly Electricity Consumption

Relation of blood-free to blood-inclusive postmortem lung water measurements in sheep

1985 ◽  
Vol 59 (2) ◽  
pp. 592-596 ◽  
Author(s):  
J. C. Collins ◽  
J. H. Newman ◽  
N. E. Wickersham ◽  
W. K. Vaughn ◽  
J. R. Snapper ◽  
...  
Keyword(s):  
Extravascular Lung Water ◽  
Weight Ratio ◽  
Lung Homogenate ◽  
Lung Water ◽  
Before And After ◽  
The Mean ◽  
Space Expansion ◽  
Highly Correlated ◽  
Linear Regressions ◽  
Better Than

Our purpose was to see if the postmortem weight ratio of extravascular lung water to blood-free dry lung (blood-free ratio) was related to similar ratios in blood-inclusive lung and in blood. We developed linear regressions of blood-free ratio on ratios for blood-inclusive lung and blood together and for blood-inclusive lung alone for 73 sheep studied under 11 different protocols and for two subgroups of sheep, one with plasma space expansion and the other without expansion. The relation of ratios of blood-free to blood-inclusive lungs was different between the two subgroups. Although all regressions were highly correlated, the fits of the blood-free ratio on ratios for blood-inclusive lung and blood together were better than for blood-inclusive lung alone. The mean error of prediction of extravascular lung water for all sheep was significantly less for the regression of blood-free ratio on ratios for blood and blood-inclusive lung together (11 g) than for blood-inclusive lung alone (18 g). This study shows that weights of lung homogenate and blood samples before and after simple oven drying can be used to provide accurate inexpensive estimates of postmortem extravascular lung water.

scholarly journals ANALISA UNJUK KERJA MESIN STEMPEL FLASH MADE IN TIONGKOK MEREK FLAZ TERHADAP MESIN STEMPEL FLASH MADE IN INDONESIA MEREK MD

2020 ◽  
Vol 2 (1) ◽  
pp. 13-18
Author(s):  
Slamet Raharjo ◽  
Massus Subekti ◽  
Imam Arif Raharjo
Keyword(s):  
Grounded Theory ◽  
Electricity Consumption ◽  
Operation Time ◽  
Electrical Energy ◽  
Quality Parameter ◽  
Theory Approach ◽  
Machine Performance ◽  
Work Method ◽  
Better Than ◽  
Made In

This research aimed to find out the work method of flash stamp machine made in Tiongkok brand Flaz and flash stamp machine made in Indonesia brand MD observed from each machine performance including colour stamp quality resulted, duration in its operation, as well as power and electricity consumption. The research method adopted is qualitative method with grounded theory approach. This research conducted in Enterprise of Flash Stamp Machine made in Indonesia brand MD on Jl. Lembang Baru I West Sudimara, Ciledug, Tangerang, Banten. The result drawn from work method research of both flash stamp machine are: First,   the stamp quality resulted by flash stamp machine brand MD was better than flash stamp machine brand MD. Second, the operation time of flash stamp machine brand MD was 4 second faster, that is 3 second, while flash sta mp machine brand Flaz was 4 second. Third, the electricity power consumption of flash stamp machine brand Flaz was smaller that is 136,62 watt, while brand Flaz was 392,34 watt. Fourth, the electrical energy consumption of flash stamp machine Flaz was smaller that is 888,39 Joule, while flash stamp machine brand MD was 1709,06. The conclusion drawn from work method research of flash stamp machine made in Tiongkok brand Flaz toward flash stamp machine made in Indonesia brand MD measured from stamp output quality parameter and operation time speed, so flash stamp machine made in Indonesia brand MD is better than flash stamp machine made in Tiongkok brand Flaz. Abstrak Penelitian ini bertujuan untuk mengetahui unjuk kerja mesin stempel flash made in Tiongkok merek Flaz terhadap mesin stempel flash made in Indonesia merek MD dilihat dari performa masing-masing mesin meliputi kualitas cap stempel warna yang dihasilkannya, lama waktu pengoperasiannya, pemakaian daya serta konsumsi energi listriknya. Metode penelitian yang di gunakan adalah metode kualitatif dengan pendekatan penelitian grounded theory. Penelitian ini dilakukan di Perusahaan Pembuatan Mesin Stempel Flash made In Indonesia merek MD di Jl. Lembang Baru I Kelurahan Sudimara Barat, Ciledug, Tangerang, Banten. Hasil yang diperoleh dari penelitian unjuk kerja kedua mesin stempel flash ini adalah :  Pertama, kualitas cap stempel yang dihasilkan mesin stempel flash merek MD lebih bagus dibandingkan mesin stempel flash merek Flaz. Kedua, lama waktu operasinya 4 detik lebih cepat mesin stempel flash merek MD yaitu selama 3 detik dan 4 detik untuk mesin stempel flash merek Flaz. Ketiga, daya listrik yang dibutuhkan lebih kecil me sin stempel flash merek Flaz yaitu sebesar 136,62 watt dan 392,34 Watt untuk merek Flaz. Keempat, konsumsi energi listrik yang dibutuhkan lebih kecil mesin stempel merek Flaz yaitu 888,39 Joule dan 1709,06 Joule untuk mesin stempel flash merek MD. Kesimpulan yang diperoleh dari penelitian unjuk kerja mesin stempel flash made in Tiongkok merek Flaz terhadap mesin stempel flash made in Indonesia merek MD diukur dari parameter kualitas hasil cap dan kecepatan waktu operasi maka mesin stempel flash made in Indonesia merek MD lebih bagus dari pada mesin stempel flash made in Tiongkok merek Flaz.

scholarly journals Sensorless dual axis solar tracker using improved sun position algorithm

2020 ◽  
Vol 11 (3) ◽  
pp. 1305
Author(s):  
Chan Men Loon ◽  
Muhamad Zalani Daud
Keyword(s):  
Low Cost ◽  
Percentage Error ◽  
Total Output ◽  
Solar Pv ◽  
Pv Systems ◽  
Measured Angle ◽  
Solar Tracker ◽  
Fixed Panel ◽  
Fixed System ◽  
Better Than

This paper presents development of a prototype sensorless dual axis solar tracker for maximum extraction of solar energy. To prove the concept and evaluate the proposed algorithm, a low cost widely availabe materials were used which was programmed based on Arduino microcontroller. The porposed algorithm works based on two search methods namely the global search that approximates the best point location in a region, and local search that further determines the actual sun’s position. Experimental results showed that the proposed algorithm gives better performance compared to the existing sun position algorithm (SPA) - based method as well as the fixed panel system. In terms of total output power, the proposed algorithm gives 17.96% more efficient than the fixed system and 6.38% better than the SPA-based system. Furthermore, the percentage error of the experimental measured angle to the actual sun azimuth angle was relatively minimal (less than 3%) during clear day operation. The system was proven to be effective in tracking the sun for improved energy production of solar PV panels and the proposed algorithm also can be used for designing the tracker with larger size of solar PV systems.

scholarly journals Monthly Electricity Consumption Forecasting Method Based on X12 and STL Decomposition Model in an Integrated Energy System

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Tianhe Sun ◽  
Tieyan Zhang ◽  
Yun Teng ◽  
Zhe Chen ◽  
Jiakun Fang
Keyword(s):  
Power Consumption ◽  
Rapid Development ◽  
Electricity Consumption ◽  
Energy System ◽  
Small Scale ◽  
Power Demand ◽  
Distributed Energy ◽  
Strategic Competition ◽  
Integrated Energy System ◽  
Monthly Electricity Consumption

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

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