Electrical energy demand forecasting model using artificial neural network: A case study of Lagos State Nigeria

Abdulsalam, Khadeejah Adebisi; Babatunde, Olubayo Moses

doi:10.5267/j.ijdns.2019.5.002

Cited by 10 publications

(7 citation statements)

References 10 publications

(19 reference statements)

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“…Cockburn, Henderson, and Stern [9] reported further that China was likely to be the largest beneficiary as it dominates the AI market in Asia-Pacific by as high as 70 percent. These findings concur with those that were established by Cohen [10], who held AI-enabled automated transportation responsible for this increase; or by De Backer, DeStefano, Menon, and Suh [11] who also confirmed that China was likely to embrace fleet-wide traffic flow through AI applications, hence elevating congestion levels [1,[12][13][14][15][16][17][18].…”

Section: Literature Reviewsupporting

confidence: 88%

Economic Impact of Artificial Intelligence: New Look for the Macroeconomic Assessment in Asia-Pacific Region

Haseeb¹,

Sasmoko²,

Mihardjo³

et al. 2019

IJCIS

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To determine the impact of artificial intelligence (AI) on the selected economies in the Asia-Pacific region. Methods: This secondary research collected data from macroeconomic and AI-specific data sets. The sources of data from which insights were gained included digital technology sectors and corporations and their functions. The focus was on the need to assess the capability of AI on business operations The macroeconomic data was collected from data resources of international organizations' including the World Economic Forum, the Organization for Economic Cooperation and Development (OECD), the World Intellectual Property Organization (WIPO), and the International Telecommunication Union (ITU). In addition, this study has considered 19 economic indicators to analyze the economic outcome of AI in selected economies of Asia-Pacific. Results: From the results, the period between 2014 and 2016 witnessed China leading with over 25,000 citable documents on the AI topic. Regarding institutions that were observed to publish over 500 times on the AI topic, the countries in the ascending order include China (600), Hong Kong (1,100), and Singapore (2,000). As such, this study established that Asia-Pacific economies such as Hong Kong and Singapore though have smaller populations, but the majority of their higher education institutions have made a significant contribution to AI research; with the small economies also having a relatively higher number of computer scientists among the top 1,000 individuals. Additionally, through empirically analyses, during 1998-2016 with annual observations, it is found that various economic outcomes of AI were presented in 8 economies of targeted region. Limitations: At first, the future outlook of AI is just discussed in conceptual meaning while empirical context still needs to be examined in upcoming studies. At second, covering the overall South Asian region provides better findings with more generalization which is missing in current research. At third, other dimensions of AI and economy like implication of AI impact index and its relationship with macroeconomic variables is also missing in current research which could be reconsidered in coming studies. Conclusion: It is evident that AI exhibits the potential to be the main driver of Asia-Pacific's economic growth. Relative to the net and gross effect of AI on labor markets and the gross domestic product (GDP) of the top Asia-Pacific economies demonstrate that by 2030, AI might yield a 16-percent increase in output, translating into an estimated amount of $13 trillion. Overall, it is concluded that Asia-Pacific, when compared to developed regions such as North America, is lagging but the availability of a large pool of user data implies that the region can move ahead-given better resource and talent allocation.

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Section: Literature Reviewsupporting

confidence: 88%

Economic Impact of Artificial Intelligence: New Look for the Macroeconomic Assessment in Asia-Pacific Region

Haseeb¹,

Sasmoko²,

Mihardjo³

et al. 2019

IJCIS

View full text Add to dashboard Cite

show abstract

“…To forecast electricity demand, ANN, RNN, SVR, PSO, MARS, ARIMA, SARIMA, MCDA, Regression, and LAEP software methods were used in several studies [5,6,19,20]. In general, socio-economic indicators, such as GDP and GDP per capita, energy importsexports, unemployment percentage, inflation percentage, population, average winter temperature and average summer temperature, regional development factor, and monthly electricity consumption values are used as independent variables.…”

Section: Introductionmentioning

confidence: 99%

“…In general, socio-economic indicators, such as GDP and GDP per capita, energy importsexports, unemployment percentage, inflation percentage, population, average winter temperature and average summer temperature, regional development factor, and monthly electricity consumption values are used as independent variables. Abdulsalama and Babatundea [5] have forecasted Lagos State electrical energy demand using an Artificial Neural Network (ANN)-based method. To forecast the performance of the presented technique, the results have been compared with actual data.…”

Section: Introductionmentioning

confidence: 99%

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

2022

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This study reviews a selection of approaches that have used Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Multi Linear Regression (MLR) to forecast electricity demand for Gokceada Island. Artificial Neural Networks, Particle Swarm Optimization, and Linear Regression methods are frequently used in the literature. Imports, exports, car numbers, and tourist-passenger numbers are used as based on input values from 2014 to 2020 for Gokceada Island, and the electricity energy demands up to 2040 are estimated as an output value. The results obtained were analyzed using statistical error metrics such as R2, MSE, RMSE, and MAE. The confidence interval analysis of the methods was performed. The correlation matrix is used to show the relationship between the actual value and method outputs and the relationship between independent and dependent variables. It was observed that ANN yields the highest confidence interval of 95% among the method utilized, and the statistical error metrics have the highest correlation for ANN methods between electricity demand output and actual data.

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“…Concerning the artificial intelligence models, ref. [13] developed a model for forecasting electrical demand using a neural network for Lagos State in Nigeria. The novel model is effective for energy forecasting due to its training capability.…”

Section: Introductionmentioning

confidence: 99%

A hybrid model for forecasting the consumption of electrical energy in a smart grid

Souhe

Mbey

Boum

et al. 2022

The Journal of Engineering

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This paper develops a novel hybrid model for forecasting electrical consumption based on several deep learning and optimization models such as Support Vector Regression (SVR), Firefly Algorithm (FA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The process is focused on the minimization of error and risk. FA is used to optimize the forecasting performance using its higher optimization ability. The proposed SVR-FA-ANFIS model is based on historical data from smart meters in order to estimate and improve the accuracy of forecasting electrical consumption. Several accuracy coefficients such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to characterize the superior performance of the proposed model. The consumption data in Cameroon over the 24-year period are used to evaluate the performance of the models. The simulation results show that the proposed method outperforms other models such as Long Short-Term Memory (LSTM) and Random Forest (RF).

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Electrical energy demand forecasting model using artificial neural network: A case study of Lagos State Nigeria

Cited by 10 publications

References 10 publications

Economic Impact of Artificial Intelligence: New Look for the Macroeconomic Assessment in Asia-Pacific Region

Economic Impact of Artificial Intelligence: New Look for the Macroeconomic Assessment in Asia-Pacific Region

Electricity Demand Forecasting with Use of Artificial Intelligence: The Case of Gokceada Island

A hybrid model for forecasting the consumption of electrical energy in a smart grid

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