scholarly journals Prediction of the Energy Demand Trend in Middle Africa—A Comparison of MGM, MECM, ARIMA and BP Models

2019 ◽  
Vol 11 (8) ◽  
pp. 2436 ◽  
Author(s):  
Wang ◽  
Zhan ◽  
Li
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Autoregressive Integrated Moving Average ◽  
Research Level ◽  
Different Types ◽  
Curve Method ◽  
The Mean ◽  
Bp Neural Network Model ◽  
Absolute Percent Error

Africa has abundant energy resources, but African energy research level is relatively low. In response to this gap, this paper takes Middle Africa as an example to systematically predict energy demand to give support. In this paper, we utilize four models, metabolic grey model (MGM), modified exponential curve method (MECM), autoregressive integrated moving average (ARIMA) and BP neural network model (BP), to predict the energy consumption of Middle Africa in the next 14 years. Comparing four completely different types of predictive models can fully depict the characteristics of the predictive data and give an all-round analysis of the predicted results. These proposed models are applied to simulate Middle Africa’s energy consumption between 1994 and 2016 to test their accuracy. Among them, the mean absolute percent error (MAPE) of MGM, MECM, ARIMA and BP are 2.41%, 4.80%, 1.91%, and 0.88%. The results show that MGM, MECM, ARIMA, and BP presented in this paper can produce reliable forecasting results. Therefore, the four models are used to forecast energy demand in the next 14 years (2017–2030). Forecasts show that energy demand of Middle Africa will continue to grow at a rate of about 5.37%.

scholarly journals The Forecasting of Electrical Energy Consumption in Morocco with an Autoregressive Integrated Moving Average Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Mohammed Jamii ◽  
Mohamed Maaroufi
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Primary Energy ◽  
Electrical Energy Consumption ◽  
Electricity Grid ◽  
Autoregressive Integrated Moving Average ◽  
Annual Period

The national demand for primary energy has experienced an average increase of almost 5% in recent years, driven by the growth in electricity consumption, which grew by an average of 6% per year between 2003 and 2017, by virtue of the almost generalization of rural electrification and the dynamism of our economy and especially the policy of major works in infrastructure, industry, agriculture, tourism, and social housing. In fact, forecasting the demand for electrical energy remains a controversial issue in the development of the electricity grid and energy management. The ARIMA (1, 1, 1) model is applied to model electrical energy consumption for the annual period from 1971 to 2020. The same data are also used to predicting for 2021–2030 in order to verify the adequacy of the model and to provide information on the state of energy demand in Morocco in the future. The main results indicate an upward trend in electrical energy consumption by the end of 2030, with electricity consumption expected to be in the range of 2039639.09–53589.00 GWh per year.

scholarly journals Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Forecasting the incidence of acute haemorrhagic conjunctivitis in Chongqing: a time series analysis

2020 ◽  
Vol 148 ◽  
Author(s):  
Hongfang Qiu ◽  
Dewei Zeng ◽  
Jing Yi ◽  
Hua Zhu ◽  
Ling Hu ◽  
...  
Keyword(s):  
Moving Average ◽  
Development Trend ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Peak Period ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Incidence Data ◽  
The Mean ◽  
Actual Incidence

Abstract Acute haemorrhagic conjunctivitis is a highly contagious eye disease, the prediction of acute haemorrhagic conjunctivitis is very important to prevent and grasp its development trend. We use the exponential smoothing model and the seasonal autoregressive integrated moving average (SARIMA) model to analyse and predict. The monthly incidence data from 2004 to 2017 were used to fit two models, the actual incidence of acute haemorrhagic conjunctivitis in 2018 was used to validate the model. Finally, the prediction effect of exponential smoothing is best, the mean square error and the mean absolute percentage error were 0.0152 and 0.1871, respectively. In addition, the incidence of acute haemorrhagic conjunctivitis in Chongqing had a seasonal trend characteristic, with the peak period from June to September each year.

scholarly journals Comparing Statistical Models to Predict Dengue Fever Notifications

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Arul Earnest ◽  
Say Beng Tan ◽  
Annelies Wilder-Smith ◽  
David Machin
Keyword(s):  
Dengue Fever ◽  
Statistical Models ◽  
Disease Surveillance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Percentage Error ◽  
Mosquito Vector ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the femaleAedes aegyptimosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.

Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models

2014 ◽  
Vol 672-674 ◽  
pp. 2085-2097 ◽  
Author(s):  
Sue Ling Lai ◽  
Ming Liu ◽  
Kuo Cheng Kuo ◽  
Ray Chang
Keyword(s):  
Hong Kong ◽  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Critical Role ◽  
Forecast Accuracy ◽  
Demand Forecast ◽  
Forecast Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

scholarly journals 2030 Energy Pathways in Côte d’Ivoire: “A Business as Usual” Analysis

2019 ◽  
Vol 7 (6) ◽  
Author(s):  
Yessoh Gaudens Thecle Edjoukou ◽  
Bangzhu Zhu ◽  
Minxing Jiang ◽  
Akadje Jean Roland Edjoukou
Keyword(s):  
Energy Demand ◽  
Moving Average ◽  
Resource Planning ◽  
Primary Energy ◽  
Steady Increase ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Autoregressive Integrated Moving Average ◽  
Primary Energy Demand ◽  
Business As Usual

Forecasting future energy demand values is of paramount importance for proper resource planning. This paper examines energy outlook for the coming decade in Côte d’Ivoire presented as a business as usual scenario. We, therefore, build a forecasting model using the Autoregressive Integrated Moving Average (ARIMA) to estimate primary energy demand and energy demand by fuels. The results indicate that energy demand will increase steadily within the forecasted period (2017-2030). However, the annual growth rate of each fuel,, including the primary energy demand item, will first rise from the year 1990 to the year 2016 and then decrease within the forecasted period except hydropower that will experience a steady increase from 1990 to 2030. Furthermore, it is noticed that the energy structure of the country will still be biofuels (fuelwood and charcoal) intensive with a significant presence of conventional sources of energy. Based on these findings, we propose some policy recommendations.

scholarly journals Forecasting of Potato Prices in India: An Application of Arima Model

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Ravi Ranjan Kumar
Keyword(s):  
West Bengal ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Madhya Pradesh ◽  
Series Data ◽  
Market Prices ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percent Error

In the present paper, Autoregressive Integrated Moving Average (ARIMA) models developed to forecast the prices of potato using time series data of eighteen years from 2002-2019. The best models selected by comparing Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE). The study revealed that ARIMA (1,1,2), ARIMA (2,1,1)(0,0,2)[12], ARIMA (2,1,2), ARIMA (1,1,4)(0,0,1)[12], ARIMA (1,1,1)(0,1,2)[12], ARIMA (0,1,0)(0,1,1)[12], and ARIMA (3,1,3) were the best fitted models for forecasting of price of potato for the states of Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura and India respectively. The prices of potato in Utter Pradesh, West Bengal and India will be increasing with the first-quarter providing the highest price. The prices of potato in Madhya Pradesh and Tripura will be highest in the fourth quarter. In Punjab, the prices of potato will be increasing with the third-quarter. The forecast shows that market prices of potato in Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura, and overall India would be ruling in the highest value of .1208 `/qt, 1812 `/qt, 1345 `/qt, 1712 `/qt, 1354 `/qt, 2636 `/qt, and 1715 `/qt respectively for the year 2020.

Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain
Keyword(s):  
Energy Consumption ◽  
Urban Development ◽  
Predictive Models ◽  
Energy Demand ◽  
Smart Home ◽  
Predictive Analytics ◽  
Moving Average ◽  
Energy Usage ◽  
Total Energy Consumption ◽  
Home Appliances

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

Periodic occurrences of annual rainfalls in Eastern India [UPH No. 9 (theme: Variability of extremes) and UPH No.19 (theme: Modelling methods)]

2020 ◽  
Author(s):  
Dr Subhabrata Panda
Keyword(s):  
Polynomial Regression ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Data Series ◽  
Eastern India ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

<p>Long period annual rainfall data series from nine raingauge stations throughout eastern India were analysed. Those data series were for the years 1901 to 1965 for Aijal (Mizoram); 1901 to 1984 for Imphal (Manipur); 1901 to 1986 for Guwahati (Assam), Shillong, Cherrapunji (Meghalaya); 1901 to 1987 for Cuttack (Odisha), Patna (Bihar), Agartala (Tripura), Krishnanagar (West Bengal). Incomplete annual rainfall data were found out by taking average of data of preceding and following years. Each annual rainfall series was divided into modelled period (1901 to 1980 for eight stations except Aijal with 1901 to 1960) and predicted period (data for years left in the series after modelled period for evaluation of the model for prediction of future rainfalls). Each annual rainfall series in the modelled period was first converted into percentage values of the mean annual rainfall and then plotted against year, which showed the oscillations of the historigram about the mean line (Tomlinson, 1987 for New Zealand rainfalls). Such type of characteristic historigrams for all stations showed periodic nature of annual rainfalls throughout eastern India. So, autoregressive integrated moving average (ARIMA) model (Clarke, 1973) was used to evolve a useful model for prediction of future rainfalls. As the ARIMA model was biased for periodicity due to inclusion of both the ‘sin’ and ‘cos’ functions and period length as 12, modelled data series were analysed for polynomial regression. The accepted degrees of polynomials were decided on the basis of analysis of variance (ANOVA). Acceptance of either ARIMA model or polynomial regression was done on the basis of -test. In most of the cases in the observed historigrams the lengths of periods were less than eight years and in some cases those were eight to 12 years and from polynomial regressions in most cases the period lengths varied in between 8 to 12 years, 13 to 22 years and 23 to 37 years; and in rare cases those lengths were 38 years and more. Considering all the limitations in the observed data and 95% confidence interval for ARIMA model, a particular amount of annual rainfall occurred at about 12 years (i.e. almost resembling a Solar Cycle) and that might be concluded after minute analysis of more observed data. Recurrence of flood and drought years can be predicted from such analysis and also by following probability analysis of excess and deficit runs of annual rainfalls (Panda <em>et al</em>., 1996).</p><p>References:</p><p>Clarke, R.T.1973. Mathematical models in hydrology. FAO Irrigation and Drainage Paper No. 19. FAO of the United Nations, Rome. pp.101-108.</p><p>Panda, S.; Datta, D.K. and Das, M.N. (1996). Prediction of drought and flood years in Eastern India using length of runs of annual rainfall. J. Soil Wat. Conserv. India. 40(3&4):184-191.</p><p>          https://www.academia.edu/15034719/Prediction_of_drought_and_flood_years_in_eastern_%20%09India%20using_length_of_runs_of_annual_rainfall</p><p>Tomlinson, A.I. (1987). Wet and dry years – seven years on. Soil & Water. Winter 1987: 8-9. ISSN 0038-0695    </p>

scholarly journals S&P BSE Sensex and S&P BSE IT return forecasting using ARIMA

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Madhavi Latha Challa ◽  
Venkataramanaiah Malepati ◽  
Siva Nageswara Rao Kolusu
Keyword(s):  
Stock Returns ◽  
Stock Exchange ◽  
Moving Average ◽  
Arima Model ◽  
Descriptive Statistics ◽  
Autoregressive Integrated Moving Average ◽  
Augmented Dickey Fuller ◽  
The Mean ◽  
Daily Stock Returns

AbstractThis study forecasts the return and volatility dynamics of S&P BSE Sensex and S&P BSE IT indices of the Bombay Stock Exchange. To achieve the objectives, the study uses descriptive statistics; tests including variance ratio, Augmented Dickey-Fuller, Phillips-Perron, and Kwiatkowski Phillips Schmidt and Shin; and Autoregressive Integrated Moving Average (ARIMA). The analysis forecasts daily stock returns for the S&P BSE Sensex and S&P BSE IT time series, using the ARIMA model. The results reveal that the mean returns of both indices are positive but near zero. This is indicative of a regressive tendency in the long-term. The forecasted values of S&P BSE Sensex and S&P BSE IT are almost equal to their actual values, with few deviations. Hence, the ARIMA model is capable of predicting medium- or long-term horizons using historical values of S&P BSE Sensex and S&P BSE IT.

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