Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

Tamil Nadu ◽

Consumer Price Index ◽

Electricity Consumption ◽

Optimization Methods ◽

Electricity Demand ◽

The State ◽

Artificial Neural ◽

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015, the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the “as-it-is” scenario, the second scenario is based on milestones set for achieving goals of “Vision 2023” document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

10.20944/preprints201801.0216.v1 ◽

2018 ◽

Author(s):

Atul Anand ◽

L Suganthi

Keyword(s):

Neural Network ◽

Tamil Nadu ◽

Consumer Price Index ◽

Electricity Consumption ◽

Optimization Methods ◽

Electricity Demand ◽

The State ◽

Artificial Neural ◽

In the present study, a hybrid optimizing algorithm has been proposed using Genetic Algorithm (GA)and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of electricity demand of the state of Tamil Nadu in India. The GA-PSO model optimizes the coefficients of factors of gross state domestic product (GSDP) , electricity consumption per capita, income growth rate and consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as ANN-BP, ANN-GA, ANN-PSO models. Further the paper also forecasts the electricity demand of Tamil Nadu based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that a direct causality exists between GSDP and the electricity demand of the state.

Hybrid GA-PSO Optimization of Artificial Neural Network for Forecasting Electricity Demand

10.20944/preprints201711.0190.v1 ◽

2017 ◽

Author(s):

Atul Anand ◽

L Suganthi

Keyword(s):

Neural Network ◽

Tamil Nadu ◽

Consumer Price Index ◽

Optimization Methods ◽

Electricity Demand ◽

The State ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Optimizing Algorithm

In  the present study, a hybrid  optimizing algorithm has been proposed using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for Artificial Neural Network (ANN) to improve the estimation of  electricity demand of  the state of Tamil Nadu in India. The GA-PSO model optimizes  the coefficients of factors of  gross state domestic product (GSDP), per capita demand, income and  consumer price index (CPI) that affect the electricity demand. Based on historical data of 25 years from 1991 till 2015 , the simulation results of GA-PSO models  are having greater accuracy and reliability than single optimization methods based on either PSO or GA. The forecasting results of ANN-GA-PSO are better than models based on single optimization such as  ANN-BP, ANN-GA, ANN-PSO models. Further  the paper also forecasts the electricity demand of Tamil Nadu  based on two scenarios. First scenario is the "as-it-is" scenario , the second scenario  is based on milestones set for achieving goals of "Vision 2023" document for the state. The present research also explores the causality between the economic growth and electricity demand in case of Tamil Nadu. The research indicates that the direct causality exists between  GSDP and the electricity demand of the state.

PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)

eLEKTRIKA ◽

10.26623/elektrika.v11i2.1699 ◽

2019 ◽

Vol 11 (2) ◽

pp. 21

Author(s):

Mukti Dwi Cahyo ◽

Sri Heranurweni ◽

Harmini Harmini

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Peak Load ◽

Electricity Demand ◽

Training Data ◽

Radial Basis ◽

Artificial Neural ◽

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.

Wind energy location prediction between meteorological stations using ANN

Global NEST Journal ◽

10.30955/gnj.001462 ◽

2015 ◽

Vol 16 (6) ◽

pp. 1135-1144

Keyword(s):

Neural Network ◽

Wind Speed ◽

Wind Energy ◽

Tamil Nadu ◽

Energy Potential ◽

Soft Computing Techniques ◽

Wind Energy Potential ◽

Artificial Neural ◽

<div> <p>Wind Energy is one of the important sources of renewable energy. There is a need to prepare the availability of wind energy in the area where there is no measured wind speed data. For this type of situation, it seems to be necessary to predict the wind energy potential using such as wind speed using artificial neural network (ANN) method. Soft computing techniques are widely used now days in the study of wind energy potential estimation. In this study the wind energy potential between neighborhood meteorological tower stations is predicted using Artificial Neural Network technique. One of the most suitable areas of Tamil Nadu for wind power generation is some locations in the districts of Tirunelveli, Thoothukudi, Kanyakumari, Theni, Coimbatore, and Dindigul. Along the southeast coastline of Tamil Nadu there are no valleys and mountains besides the mountains are situated away from the sea coast in many regions. Therefore, these regions are exposed to northerly winds that are not as strong as the southerly winds.</p> </div> <p> </p>

Artificial Neural Network Algorithms for 3D Printing

Materials ◽

10.3390/ma14010163 ◽

2020 ◽

Vol 14 (1) ◽

pp. 163

Author(s):

Muhammad Arif Mahmood ◽

Anita Ioana Visan ◽

Carmen Ristoscu ◽

Ion N. Mihailescu

Keyword(s):

Neural Network ◽

Machine Learning ◽

3D Printing ◽

Process Parameters ◽

Optimization Methods ◽

Physical Models ◽

Network Algorithms ◽

Artificial Neural ◽

Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

Predicting the Land Value using Regression Techniques and Artificial Neural Network

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a1021.1291s419 ◽

2019 ◽

Vol 9 (1S3) ◽

pp. 431-436

Keyword(s):

Neural Network ◽

Tamil Nadu ◽

Research Work ◽

Land Value ◽

Regression Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Cost ◽

Cost Of Construction

Land value can be an important factor which influences the cost of construction on working in the project. The land has socio-economic and environmental values and the confronted problems on land involves the increasing costs for developing the land such as built up, agricultural, residential, commercial and industrial areas. Hence this paper concentrates on prediction of land value by considering some important factors that affects it. The study area has been selected under Tirupur district, being a developing one in Tamil Nadu. The eleven areas in four different taluks under Tirupur district were chosen for research work. The average values of monthly variation are taken for the chosen factor for the years from 2001 to 2017. Using regression analysis and artificial neural network, the prediction has been done for the future land value. The performance of both the model executed good and fit for forecasting results. Though both the model showed better results, Artificial Neural Network (ANN) showed accuracy than regression method.

Studi Estimasi Beban Puncak Hari Libur Nasional Sistem Interkoneksi Sulselbar Menggunakan Jaringan Syaraf Tiruan Propagasi Balik

Jurnal Sinergi Jurusan Teknik Mesin ◽

10.31963/sinergi.v18i2.2627 ◽

2020 ◽

Vol 18 (2) ◽

pp. 170

Author(s):

Makmur Saini ◽

A.M. Shiddiq Yunus ◽

Firdaus Firdaus

Keyword(s):

Neural Network ◽

Electric Power ◽

Electricity Consumption ◽

Electricity Supply ◽

Load Planning ◽

South Sulawesi ◽

Power Load ◽

Artificial Neural ◽

The development of population from time to time is increased and the need for electricity consumption is also increased. Simultaneously increasing the need for electric power requires the provider of electricity supply in this case PT. PLN (Persero) should deliver power effectively to consumers. To maximize the distribution of electricity to the consumer, a load planning is required. This load planning is conducted to find out how much amount of power is required by consumer. The purpose of this research is to investigate the need of power load of interconnection system of South Sulawesi and to investigate the accuracy of load planning using Artificial Neural Network (ANN) backpropagation.

ARTIFICIAL NEURAL NETWORKS APPLICATION TO PREDICTION OF ELECTRICITY CONSUMPTION

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr201231003a ◽

2021 ◽

Vol 20 (1) ◽

pp. 033

Author(s):

Miona Andrejević Stošović ◽

Novak Radivojević ◽

Igor Jovanović ◽

Andrija Petrušić

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Electricity Market ◽

Electricity Consumption ◽

Ann Model ◽

Statistical Tools ◽

Artificial Neural ◽

In this paper, we will present an artificial neural network (ANN) model trained to forecast hourly electricity consumption of energy in industry for a day-ahead. We will start with a brief analysis of the global electricity market with a special reference to the Serbian market. Next, the daily electricity consumption amounts between August 1st and December 19th 2019 will be analyzed using statistical tools. According to the obtained results, we will give predictions of our models, based on different number of previous days.