Analysis Of Testing The Forecast For The Electric Energy Needs In North Sumatra Province Using The Backpropagation Neural Network Method

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Introduction

Meeting the increasing demand for electrical energy is a significant challenge for energy providers. To anticipate this, accurate estimated methods are needed to take strategic steps in meeting future energy needs. This study analyzed the needs of electrical energy in the North Sumatra region in the 2015-2018 period using the backpropagation artificial nerve network method.

Background

The North Sumatra region is one of the provinces in Indonesia that experiences a significant increase in electrical energy demand. The region's economy is growing rapidly, driven by various sectors such as industry, commerce, and services. To meet the increasing demand for electrical energy, it is essential to have an accurate estimate of the region's energy needs.

Methodology

This study uses historical data in 2011-2014 as training data. The data used include:

  • Number of customers (household, industrial, business, social) sector
  • Total population
  • GRDP of North Sumatra

The data is then processed using the backpropagation neural network method. After a number of training experiments, the best network architecture was obtained consisting of 6 inputs, 3 neurons on hidden layers, and 1 neuron in the output layer that represented electrical energy consumption in GWH units.

Results

The estimated results using this method are then compared with Real Data 2015-2018. The analysis shows very accurate results with an average error (mape) of 1.037% and MSE value 2.40E-24. That is, the estimated results have a difference of less than 10% compared to real data.

Advantages of the Neural Network Backpropagation Method

The backpropagation neural network method has several advantages in predicting electrical energy needs:

Ability to Adapt

Artificial neural networks are able to learn and adjust to complex and dynamic data patterns, so that they can provide more accurate estimates.

High Accuracy

As shown in the results of this study, the backpropagation neural network method shows a high level of accuracy in predicting the needs of electrical energy.

Flexibility

This method can be applied to various types of data and can be easily modified to adjust the needs of the analysis.

Utilization of Research Results

The results of this study have great benefits for related parties, in particular:

PT PLN (Persero)

Accurate Electric Energy Energy Estimates Data can be used as a basis for determining the electrical energy distribution strategy, building infrastructure, and determining the energy generation capacity needed.

Government

This data can help the government in developing appropriate energy policies to encourage economic growth and community welfare.

Industry

This data can help the industry in planning electricity needs and managing operational costs more effectively.

Conclusion

This study shows that the backpropagation neural network method is an effective method for predicting the needs of electrical energy in the North Sumatra region. Accurate estimates can help stakeholders in making strategic decisions in managing electrical energy and encouraging sustainable economic growth.

Recommendations

Based on the results of this study, the following recommendations are made:

  • Further Research

Further research is needed to validate the results of this study and to explore the application of the backpropagation neural network method in other regions.

  • Implementation

The results of this study should be implemented in the planning and management of electrical energy in the North Sumatra region.

  • Capacity Building

Capacity building is needed to ensure that stakeholders have the necessary skills and knowledge to apply the backpropagation neural network method in predicting electrical energy needs.

Limitations

This study has several limitations, including:

  • Data Quality

The quality of the data used in this study may affect the accuracy of the results.

  • Model Complexity

The complexity of the backpropagation neural network method may make it difficult to interpret the results.

  • Generalizability

The results of this study may not be generalizable to other regions.

Future Research Directions

Future research directions include:

  • Validation

Validation of the results of this study using other data sets.

  • Application

Application of the backpropagation neural network method in other regions.

  • Comparison

Comparison of the backpropagation neural network method with other methods in predicting electrical energy needs.

References

  • [1] [Reference 1]
  • [2] [Reference 2]
  • [3] [Reference 3]

Note: The references should be listed in a separate section at the end of the article.

Introduction

In our previous article, we discussed the analysis of testing the forecast for the electric energy needs in North Sumatra Province using the backpropagation neural network method. In this article, we will answer some of the frequently asked questions related to this topic.

Q: What is the backpropagation neural network method?

A: The backpropagation neural network method is a type of artificial neural network that is used to predict the output of a system based on the input data. It is a supervised learning algorithm that uses a multilayer perceptron to learn the relationship between the input and output data.

Q: How does the backpropagation neural network method work?

A: The backpropagation neural network method works by first training the network on a set of input-output pairs. The network learns the relationship between the input and output data by adjusting the weights and biases of the connections between the neurons. Once the network is trained, it can be used to predict the output of the system for new input data.

Q: What are the advantages of using the backpropagation neural network method?

A: The backpropagation neural network method has several advantages, including:

  • Ability to adapt: Artificial neural networks are able to learn and adjust to complex and dynamic data patterns, so that they can provide more accurate estimates.
  • High accuracy: As shown in the results of this study, the backpropagation neural network method shows a high level of accuracy in predicting the needs of electrical energy.
  • Flexibility: This method can be applied to various types of data and can be easily modified to adjust the needs of the analysis.

Q: What are the limitations of using the backpropagation neural network method?

A: The backpropagation neural network method has several limitations, including:

  • Data quality: The quality of the data used in this study may affect the accuracy of the results.
  • Model complexity: The complexity of the backpropagation neural network method may make it difficult to interpret the results.
  • Generalizability: The results of this study may not be generalizable to other regions.

Q: How can the results of this study be applied in practice?

A: The results of this study can be applied in practice by using the backpropagation neural network method to predict the needs of electrical energy in other regions. This can help stakeholders in making strategic decisions in managing electrical energy and encouraging sustainable economic growth.

Q: What are the future research directions for this study?

A: Future research directions for this study include:

  • Validation: Validation of the results of this study using other data sets.
  • Application: Application of the backpropagation neural network method in other regions.
  • Comparison: Comparison of the backpropagation neural network method with other methods in predicting electrical energy needs.

Q: What are the implications of this study for the energy sector?

A: The implications of this study for the energy sector are significant. The backpropagation neural network method can be used to predict the needs of electrical energy in other regions, which can help stakeholders in making strategic decisions in managing electrical energy and encouraging sustainable economic growth.

Q: What are the implications of this study for the environment?

A: The implications of this study for the environment are also significant. The backpropagation neural network method can be used to predict the needs of electrical energy in other regions, which can help reduce the environmental impact of energy production and consumption.

Conclusion

In conclusion, the backpropagation neural network method is a powerful tool for predicting the needs of electrical energy in North Sumatra Province. The results of this study show that the method is highly accurate and can be used to make strategic decisions in managing electrical energy and encouraging sustainable economic growth. Future research directions include validation, application, and comparison of the backpropagation neural network method with other methods in predicting electrical energy needs.