Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression

Summary In this paper, a proportional nucleolus game theory (PNGT)–based iterative method has been presented to compute locational marginal price (LMP) at buses where distributed generation (DG) units were installed in a distribution network. Proportional nucleolus theory is one of the solution concepts for a cooperative game theory problem. The PNGT‐based iterative method provides financial incentives in terms of LMP to DG owners as per their contribution in loss reduction and emission reduction at a particular loading on the distribution system. In this method, LMP values depend on the distribution company's decision maker's preference among loss reduction, emission reduction, and distribution company's additional benefit. This proposed PNGT‐based iterative method has been implemented on a Taiwan power company's distribution network consisting of 84 buses with 15 DG units using MATLAB. The computed LMP values have operated the network based on decision‐maker priority so as to enable fair competition among DG owners.

show abstract

“…22 The testing performance of the network was measured by using the mean absolute percentage error and root mean square error. 23…”

Section: Load Forecasting For Day-ahead Operationmentioning

confidence: 99%

Proportional nucleolus game theory-based locational marginal price computation for loss and emission reduction in a radial distribution system

Veeramsetty

Venkaiah

Kumar

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…According to the structure of the model, it can be divided into the single prediction method and combined prediction method. Among them, single prediction methods include the time series method [4][5][6], linear regression method [7][8][9], grey forecasting method [10,11], support vector machine [12][13][14], and BP neural network [15][16][17]. However, any single prediction method in practical applications, owing to their own defects, causes insufficient prediction accuracy, and it is difficult to accurately predict the future power consumption level of the region.…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumption Prediction for Xinjiang Electric Energy Replacement

Song¹,

Liang²,

Chang-zu

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In recent years, the phenomenon of wind and solar energy abandoned in Xinjiang’s new energy has become severe, the contradiction between the supply and demand of the power grid is obvious, and the proportion of power in the energy consumption structure is relatively low, thus hindering the development of Xinjiang’s green power. In this context, the focus of Xinjiang’s power has shifted to promote the development of electric energy replacement. Therefore, using the Xinjiang region as an example, we first select the important indicators such as the terminal energy substitution in Xinjiang, added value of the secondary industry, population, terminal power consumption intensity, and per capita disposable income. Subsequently, eight combined forecasting models based on the grey model (GM), multiple linear regression (MLR), and error back propagation neural network (BP) are constructed to predict and analyse the electricity consumption of the whole society in Xinjiang. The results indicate the optimal weighted combination forecasting model, GM-MLR-BP of the induced ordered weighted harmonic averaging operator (IOWHA operator), exhibits better prediction accuracy, and the effectiveness of the proposed method is proven.

show abstract

“…Various techniques have been applied in electricity consumption forecasting including holt-winters and seasonal regression [2], time series models [3], first-order fuzzy time series [4], multiple linear regression [5][6], autoregressive integrated moving average (ARIMA) [7], seasonal ARIMA (SARIMA) [8], artificial neural network (ANN) [6,[8][9][10][11][12][13], Least-square SVM (LSSVM) [13], support vector regression [9], ANFIS [14][15] and ARIMA-ANFIS [16].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Electricity Consumption Using SARIMA Method in IBM SPSS Software

Sim¹,

Tay²,

Huong³

et al. 2019

ujeee

View full text Add to dashboard Cite

Forecasting is a prediction of future values based on historical data. It can be conducted using various methods such as statistical methods or machine learning techniques. Electricity is a necessity of modern life. Hence, accurate forecasting of electricity demand is important. Overestimation will cause a waste of energy but underestimation leads to higher operation costs. Univesity Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian technical university, therefore there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The monthly UTHM electricity consumption data exhibits seasonality-periodic fluctuations. Thus, the seasonal Autoregressive Integrated Moving Average (SARIMA) method was applied in IBM SPSS software to predict UTHM electricity consumption for 2019 via Box-Jenkins method and Expert Modeler. There were a total of 120 observations taken from January year 2009 to December year 2018 to build the models. The best model from both methods is SARIMA(0, 1, 1)(0, 1, 1) 12. It was found that the result through the Box-Jenkins method is approximately the same with the result generated through Expert Modeler in SPSS with MAPE of 8.4%.

show abstract

Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression

Cited by 54 publications

References 7 publications

Proportional nucleolus game theory-based locational marginal price computation for loss and emission reduction in a radial distribution system

Proportional nucleolus game theory-based locational marginal price computation for loss and emission reduction in a radial distribution system

Electricity Consumption Prediction for Xinjiang Electric Energy Replacement

Forecasting Electricity Consumption Using SARIMA Method in IBM SPSS Software

Contact Info

Product

Resources

About