scholarly journals SARX Model Application for Industrial Power Demand Forecasting in Brazil

2013 ◽  
Vol 7 (1) ◽  
Author(s):  
João Bosco B. de Castro ◽  
Alessandra De Ávila Montini
Keyword(s):  
Power Consumption ◽  
Explanatory Power ◽  
Demand Forecasting ◽  
Capacity Utilization ◽  
Autoregressive Process ◽  
Coefficient Of Determination ◽  
Time Lags ◽  
Exogenous Variables ◽  
Environmentally Sustainable ◽  
Proposed Model

<p>The objective of this paper is to propose the application of the SARX model to arrive at industrial power consumption forecasts in Brazil, which are critical to support decisionmaking in the energy sector, based on technical, economic and environmentally sustainable grounds. The proposed model has a seasonal component and considers the influence of exogenous variables on the projection of the dependent variable and utilizes an autoregressive process for residual modeling so as to improve its explanatory power. Five exogenous variables were included: industrial capacity utilization, industrial electricity tariff, industrial real revenues, exchange rate, and machinery and equipment inflation. In addition, the model assumed that power forecast was dependent on its own time lags and also on a dummy variable to reflect 2009 economic crisis. The study used 84 monthly observations, from January 2003 to December 2009. The backward method was used to select exogenous variables, assuming a 0.10 descriptive value. The results showed an adjusted coefficient of determination of 93.9% and all the estimated coefficients were statistically significant at a 0.10 descriptive level. Forecasts were also made from January to May 2010 at a 95% confidence interval, which included actual consumption values for this period. The SARX model has demonstrated an excellent performance for industrial power consumption forecasting in Brazil.</p>

scholarly journals Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6065
Author(s):  
Sumit Saroha ◽  
Marta Zurek-Mortka ◽  
Jerzy Ryszard Szymanski ◽  
Vineet Shekher ◽  
Pardeep Singla
Keyword(s):  
Electricity Markets ◽  
Wavelet Packet ◽  
Demand Forecasting ◽  
Time Lags ◽  
Market Clearing ◽  
Mutual Correlation ◽  
Proposed Model ◽  
Yearly Average ◽  
Auto Correlation Function ◽  
Linear Neural Network

In order to analyze the nature of electrical demand series in deregulated electricity markets, various forecasting tools have been used. All these forecasting models have been developed to improve the accuracy of the reliability of the model. Therefore, a Wavelet Packet Decomposition (WPD) was implemented to decompose the demand series into subseries. Each subseries has been forecasted individually with the help of the features of that series, and features were chosen on the basis of mutual correlation among all-time lags using an Auto Correlation Function (ACF). Thus, in this context, a new hybrid WPD-based Linear Neural Network with Tapped Delay (LNNTD) model, with a cyclic one-month moving window for a one-year market clearing volume (MCV) forecasting has been proposed. The proposed model has been effectively implemented in two years (2015–2016) and unconstrained MCV data collected from the Indian Energy Exchange (IEX) for 12 grid regions of India. The results presented by the proposed models are better in terms of accuracy, with a yearly average MAPE of 0.201%, MAE of 9.056 MWh, and coefficient of regression (R2) of 0.9996. Further, forecasts of the proposed model have been validated using tracking signals (TS’s) in which the values of TS’s lie within a balanced limit between −492 to 6.83, and universality of the model has been carried out effectively using multiple steps-ahead forecasting up to the sixth step. It has been found out that hybrid models are powerful forecasting tools for demand forecasting.

scholarly journals PREVISÃO DO CONSUMO RESIDENCIAL DE ENERGIA ELÉTRICA NO BRASIL: APLICAÇÃO DO MODELO ARX DOI:10.7444/fsrj.v2i2.50

2010 ◽  
Vol 2 (2) ◽  
pp. 3-16
Author(s):  
Joao Bosco De Castro ◽  
Alessandra De Ávila Montini
Keyword(s):  
Explanatory Power ◽  
Electricity Consumption ◽  
Arx Model ◽  
Environmentally Sustainable ◽  
Explanatory Variables ◽  
Proposed Model ◽  
Residential Electricity ◽  
Residential Electricity Consumption ◽  
The Cost ◽  
Descriptive Level

This work aims to propose the application of the ARX model to forecast residential electricity consumption in Brazil. Such estimates are critical for decision making in the energy sector,  from a technical, economic and environmentally sustainable standpoint. The demand for electricity follows a multiplicative model based on economic theory and involves four explanatory variables: the cost of residential electricity, the actual average income, the inflation of domestic utilities and the electricity consumption. The coefficients of the electricity consumption equation  were determined using the ARX model, which considers the influence of exogenous variables to estimate the dependent variable and employs an autoregression process for residual modeling to improve the explanatory power. The resulting model has a determination coefficient of 95.4 percent and all estimated coefficients were significant at the 0.10 descriptive level. Residential electricity consumption estimates were also determined for January and February 2010 within the 95 percent confidence interval, which included the actual consumption figures observed. The proposed model has been shown to be useful for estimating residential electricity consumption  in Brazil. Key-words: Time series. Electricity consumption. ARX modeling. 

scholarly journals Why Is Collaborative Apparel Consumption Gaining Popularity? An Empirical Study of US Gen Z Consumers

2021 ◽  
Vol 13 (15) ◽  
pp. 8360
Author(s):  
Lindsay McCoy ◽  
Yuan-Ting Wang ◽  
Ting Chi
Keyword(s):  
Explanatory Power ◽  
Intention To Use ◽  
Fashion Leadership ◽  
Mediating Role ◽  
Need For Uniqueness ◽  
Proposed Model ◽  
The Us ◽  
Multiple Regression Method ◽  
Perceived Consumer Effectiveness ◽  
Sustainability Movement

Apparel rental, also known as collaborative apparel consumption, has created an innovative and popular business model, providing consumers with the ability to focus on using their products instead of ownership. Recent surveys show that sustainability is driving demand and customer loyalty in the US. Among all generations, Gen Z consumers lead the way. To better understand the emerging popularity of apparel rental services among Gen Z consumers who are becoming a major driving force for retail growth and the sustainability movement, this study aimed to identify the factors significantly influencing Gen Z consumers’ intention to use apparel rental services; 362 eligible responses were gathered via a questionnaire survey. The psychometric properties of the proposed model were examined, and the multiple regression method was applied to test the hypotheses. Attitude, subject norms, perceived consumer effectiveness, past environmental behavior, and fashion leadership significantly affected Gen Z consumers’ intentions to use apparel rental services. Attitude plays a mediating role between Gen Z consumers’ environmental knowledge, fashion leadership, need for uniqueness, and their intention to use apparel rental services. The proposed research model exhibited good explanatory power, accounting for 58.6% of the variance in Gen Z consumers’ use intention toward apparel rental services.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Usage of the Pareto Fronts as a Tool to Select Data in the Forecasting Process—A Short-Term Electric Energy Demand Forecasting Case

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3204
Author(s):  
Michał Sabat ◽  
Dariusz Baczyński
Keyword(s):  
Energy Demand ◽  
Demand Forecasting ◽  
Electric Energy ◽  
Forecasting Model ◽  
Short Term ◽  
Complex Evaluation ◽  
Proposed Model ◽  
Energy Demand Forecasting ◽  
Pareto Fronts ◽  
Naive Approach

Transmission, distribution, and micro-grid system operators are struggling with the increasing number of renewables and the changing nature of energy demand. This necessitates the use of prognostic methods based on ever shorter time series. This study depicted an attempt to develop an appropriate method by introducing a novel forecasting model based on the idea to use the Pareto fronts as a tool to select data in the forecasting process. The proposed model was implemented to forecast short-term electric energy demand in Poland using historical hourly demand values from Polish TSO. The study rather intended on implementing the range of different approaches—scenarios of Pareto fronts usage than on a complex evaluation of the obtained results. However, performance of proposed models was compared with a few benchmark forecasting models, including naïve approach, SARIMAX, kNN, and regression. For two scenarios, it has outperformed all other models by minimum 7.7%.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals Comparative Study of Ground Measured, Satellite-Derived, and Estimated Global Solar Radiation Data in Nigeria

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Boluwaji M. Olomiyesan ◽  
Onyedi D. Oyedum
Keyword(s):  
Solar Radiation ◽  
Comparative Study ◽  
Global Radiation ◽  
Statistical Error ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Radiation Data ◽  
Proposed Model

In this study, the performance of three global solar radiation models and the accuracy of global solar radiation data derived from three sources were compared. Twenty-two years (1984–2005) of surface meteorological data consisting of monthly mean daily sunshine duration, minimum and maximum temperatures, and global solar radiation collected from the Nigerian Meteorological (NIMET) Agency, Oshodi, Lagos, and the National Aeronautics Space Agency (NASA) for three locations in North-Western region of Nigeria were used. A new model incorporating Garcia model into Angstrom-Prescott model was proposed for estimating global radiation in Nigeria. The performances of the models used were determined by using mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE), and coefficient of determination (R2). Based on the statistical error indices, the proposed model was found to have the best accuracy with the least RMSE values (0.376 for Sokoto, 0.463 for Kaduna, and 0.449 for Kano) and highest coefficient of determination, R2 values of 0.922, 0.938, and 0.961 for Sokoto, Kano, and Kaduna, respectively. Also, the comparative study result indicates that the estimated global radiation from the proposed model has a better error range and fits the ground measured data better than the satellite-derived data.

scholarly journals Optimization Method for Forecasting Confirmed Cases of COVID-19 in China

2020 ◽  
Vol 9 (3) ◽  
pp. 674 ◽  
Author(s):  
Mohammed A. A. Al-qaness ◽  
Ahmed A. Ewees ◽  
Hong Fan ◽  
Mohamed Abd El Aziz
Keyword(s):  
Relative Error ◽  
Computing Time ◽  
World Health ◽  
Coefficient Of Determination ◽  
Flower Pollination Algorithm ◽  
Percentage Error ◽  
Anfis Model ◽  
Local Optima ◽  
Inference System ◽  
Proposed Model

In December 2019, a novel coronavirus, called COVID-19, was discovered in Wuhan, China, and has spread to different cities in China as well as to 24 other countries. The number of confirmed cases is increasing daily and reached 34,598 on 8 February 2020. In the current study, we present a new forecasting model to estimate and forecast the number of confirmed cases of COVID-19 in the upcoming ten days based on the previously confirmed cases recorded in China. The proposed model is an improved adaptive neuro-fuzzy inference system (ANFIS) using an enhanced flower pollination algorithm (FPA) by using the salp swarm algorithm (SSA). In general, SSA is employed to improve FPA to avoid its drawbacks (i.e., getting trapped at the local optima). The main idea of the proposed model, called FPASSA-ANFIS, is to improve the performance of ANFIS by determining the parameters of ANFIS using FPASSA. The FPASSA-ANFIS model is evaluated using the World Health Organization (WHO) official data of the outbreak of the COVID-19 to forecast the confirmed cases of the upcoming ten days. More so, the FPASSA-ANFIS model is compared to several existing models, and it showed better performance in terms of Mean Absolute Percentage Error (MAPE), Root Mean Squared Relative Error (RMSRE), Root Mean Squared Relative Error (RMSRE), coefficient of determination ( R 2 ), and computing time. Furthermore, we tested the proposed model using two different datasets of weekly influenza confirmed cases in two countries, namely the USA and China. The outcomes also showed good performances.

Modelling the significance of social support and entrepreneurial skills for determining entrepreneurial behaviour of individuals

2018 ◽  
Vol 14 (3) ◽  
pp. 242-266 ◽  
Author(s):  
Muhammad Shoaib Farooq
Keyword(s):  
Social Support ◽  
Explanatory Power ◽  
Entrepreneurial Intention ◽  
Subjective Norms ◽  
Policy Makers ◽  
Content Type ◽  
Entrepreneurial Behaviour ◽  
Entrepreneurial Skills ◽  
Proposed Model ◽  
The Impact

Purpose Although entrepreneurial behaviour is considered a key element for economic development, yet very less is known about the determinants of factors leading towards entrepreneurial intention and behaviour. In order to bridge this gap, the purpose of this paper is to investigate the role of social support and entrepreneurial skills in determining entrepreneurial behaviour of individuals. Developing on the base of the theory of planned behaviour (TPB), this study investigates the relationship between social support, entrepreneurial skills and entrepreneurial behaviour along with existing constructs of the TPB (i.e. attitude, subjective norms, perceived behavioural control and entrepreneurial intention). Design/methodology/approach Data was collected from 281 respondents using a simple random sampling method, and the variance-based partial least-squares, structural equation modelling (PLS-SEM) approach was used for testing the proposed conceptual model. Findings Findings of this study have validated the proposed model, which have an explanatory power of 68.3 per cent. Moreover, findings reveal that social support and entrepreneurial skills have a significant impact on entrepreneurial intention of individuals. However, an unanticipated and non-significant relation between subjective norms and entrepreneurial intention is also found. Research limitations/implications Due to the limited scope of this study, a multi-group analysis is not possible, which is considered as a limitation of this study. Moreover, due to time constraints, this study is conducted within a specified time-frame; however, a longitudinal study over a period of three to six years can overcome this limitation. Practical implications Findings of this study are expected to have substantial implications for policy makers, future researchers and academicians. Outcomes of this study can help to better understand the cognitive phenomenon of nascent entrepreneurs. Moreover, it is expected that this study can serve as a torch-bearer for policy makers to develop better entrepreneurial development programmes, policies and initiatives for promoting self-employment behaviour. Originality/value Findings of this study are a unique step forward and offer new insights towards a better understanding of the determinants of entrepreneurial behaviour. Moreover, this study extends Ajzen’s (1991) TPB in the context of entrepreneurial behaviour. By introducing and investigating the impact of two new variables, i.e. social support and entrepreneurial skills in the TPB and by validating the proposed model with PLS-SEM approach, this study makes a sizeable theoretical, methodological and contextual contribution in the overall body of knowledge.

Prediction of Design Hourly Volume on Rural Roads

2020 ◽  
pp. 036119812096556
Author(s):  
Miloš Petković ◽  
Vladan Tubić ◽  
Nemanja Stepanović
Keyword(s):  
Coefficient Of Determination ◽  
Rural Roads ◽  
The Road ◽  
Annual Average Daily Traffic ◽  
Proposed Model ◽  
Traffic Volumes ◽  
Implementation Costs ◽  
On The Road ◽  
Predicted Values ◽  
Design Hourly Volume

Design hourly volume (DHV) represents one of the most significant parameters in the procedures of developing and evaluating road designs. DHV values can be accurately and precisely calculated only on the road sections with the implemented automatic traffic counters (ATCs) which constantly monitor the traffic volume. Unfortunately, many road sections do not contain ATCs primarily because of the implementation costs. Consequently, for many years, the DHV values have been defined on the basis of occasional counting and the factors related to traffic flow variability over time. However, it has been determined that this approach has significant limitations and that the predicted values considerably deviate from the actual values. Therefore, the main objective of this paper is to develop a model which will enable DHV prediction on rural roads in cases of insufficient data. The suggested model is based on the correlation between DHVs and the parameters defining the characteristics of traffic flows, that is, the relationship between the traffic volumes on design working days and non-working days, and annual average daily traffic. The results of the conducted research indicate that the application of the proposed model enables the prediction of DHV values with a significant level of data accuracy and reliability. The coefficient of determination (R2) shows that more than 98% of the variance of the calculated DHVs was explained by the observed DHV values, while the mean error ranged from 4.86% to 7.84% depending on the number of hours for which DHV was predicted.

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