Modeling of electricity demand forecast for power system

In this study, an inexact two-stage stochastic linear programming (ITSLP) method is proposed for supporting sustainable management of electric power system under uncertainties. Methods of interval-parameter programming and two-stage stochastic programming were incorporated to tackle uncertainties expressed as interval values and probability distributions. The dispatchable loads are integrated into the framework of the virtual power plants, and the support vector regression technique is applied to the prediction of electricity demand. For demonstrating the effectiveness of the developed approach, ITSLP is applied to a case study of a typical planning problem of power system considering virtual power plants. The results indicate that reasonable solutions for virtual power plant management practice have been generated, which can provide strategies in mitigating pollutant emissions, reducing system costs, and improving the reliability of power supply. ITSLP is more reliable for the risk-aversive planners in handling high-variability conditions by considering peak-electricity demand and the associated recourse costs attributed to the stochastic event. The solutions will help decision makers generate alternatives in the event of the insufficient power supply and offer insight into the tradeoffs between economic and environmental objectives.

Download Full-text

Long Term Household Electricity Demand Forecasting Based on RNN-GBRT Model and a Novel Energy Theft Detection Method

Applied Sciences ◽

10.3390/app11188612 ◽

2021 ◽

Vol 11 (18) ◽

pp. 8612

Author(s):

Santanu Kumar Dash ◽

Michele Roccotelli ◽

Rasmi Ranjan Khansama ◽

Maria Pia Fanti ◽

Agostino Marcello Mangini

Keyword(s):

Energy Demand ◽

Demand Forecasting ◽

Sudden Change ◽

Electricity Demand ◽

Gradient Boosting ◽

Forecasting Model ◽

Demand Forecast ◽

Daily Consumption ◽

Energy Provider

The long-term electricity demand forecast of the consumer utilization is essential for the energy provider to analyze the future demand and for the accurate management of demand response. Forecasting the consumer electricity demand with efficient and accurate strategies will help the energy provider to optimally plan generation points, such as solar and wind, and produce energy accordingly to reduce the rate of depletion. Various demand forecasting models have been developed and implemented in the literature. However, an efficient and accurate forecasting model is required to study the daily consumption of the consumers from their historical data and forecast the necessary energy demand from the consumer’s side. The proposed recurrent neural network gradient boosting regression tree (RNN-GBRT) forecasting technique allows one to reduce the demand for electricity by studying the daily usage pattern of consumers, which would significantly help to cope with the accurate evaluation. The efficiency of the proposed forecasting model is compared with various conventional models. In addition, by the utilization of power consumption data, power theft detection in the distribution line is monitored to avoid financial losses by the utility provider. This paper also deals with the consumer’s energy analysis, useful in tracking the data consistency to detect any kind of abnormal and sudden change in the meter reading, thereby distinguishing the tampering of meters and power theft. Indeed, power theft is an important issue to be addressed particularly in developing and economically lagging countries, such as India. The results obtained by the proposed methodology have been analyzed and discussed to validate their efficacy.

Download Full-text

A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Sustainability ◽

10.3390/su12083103 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3103 ◽

Cited By ~ 4

Author(s):

Hyojoo Son ◽

Changwan Kim

Keyword(s):

Time Series ◽

Deep Learning ◽

Power System ◽

Electricity Demand ◽

Efficient Production ◽

Power System Planning ◽

System Planning ◽

Residential Sector ◽

Use Of Resources ◽

Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

Download Full-text

An Optimized Forecasting Approach Based on Grey Theory and Cuckoo Search Algorithm: A Case Study for Electricity Consumption in New South Wales

Abstract and Applied Analysis ◽

10.1155/2014/183095 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Ping Jiang ◽

Qingping Zhou ◽

Haiyan Jiang ◽

Yao Dong

Keyword(s):

Search Algorithm ◽

Arima Model ◽

Electricity Consumption ◽

Cuckoo Search ◽

Cuckoo Search Algorithm ◽

Electricity Demand ◽

Grey Model ◽

Grey Theory ◽

Demand Forecast ◽

Term Selection

With rapid economic growth, electricity demand is clearly increasing. It is difficult to store electricity for future use; thus, the electricity demand forecast, especially the electricity consumption forecast, is crucial for planning and operating a power system. Due to various unstable factors, it is challenging to forecast electricity consumption. Therefore, it is necessary to establish new models for accurate forecasts. This study proposes a hybrid model, which includes data selection, an abnormality analysis, a feasibility test, and an optimized grey model to forecast electricity consumption. First, the original electricity consumption data are selected to construct different schemes (Scheme 1: short-term selection and Scheme 2: long-term selection); next, the iterative algorithm (IA) and cuckoo search algorithm (CS) are employed to select the best parameter of GM(1,1). The forecasted day is then divided into several smooth parts because the grey model is highly accurate in the smooth rise and drop phases; thus, the best scheme for each part is determined using the grey correlation coefficient. Finally, the experimental results indicate that the GM(1,1) optimized using CS has the highest forecasting accuracy compared with the GM(1,1) and the GM(1,1) optimized using the IA and the autoregressive integrated moving average (ARIMA) model.

Download Full-text

Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables

European J of Industrial Engineering ◽

10.1504/ejie.2009.025049 ◽

2009 ◽

Vol 3 (3) ◽

pp. 277 ◽

Cited By ~ 15

Author(s):

F. Chui ◽

A. Elkamel ◽

R. Surit ◽

E. Croiset ◽

P.L. Douglas

Keyword(s):

Power System ◽

Demand Forecasting ◽

Electricity Demand ◽

Climatic Variables ◽

Power System Planning ◽

System Planning ◽

Electricity Demand Forecasting

Download Full-text

Electricity demand forecast of Turkey based on hydropower and windpower potential

Energy Sources Part B Economics Planning and Policy ◽

10.1080/15567249.2015.1084401 ◽

2016 ◽

Vol 12 (1) ◽

pp. 85-90 ◽

Cited By ~ 2

Author(s):

Aydın Hacı Dönmez ◽

Yakup Karakoyun ◽

Zehra Yumurtaci

Keyword(s):

Electricity Demand ◽

Demand Forecast

Download Full-text

Econometric Model for Forecasting Electricity Demand of Industry and Construction Sectors in Vietnam to 2030

Science & Technology Development Journal - Engineering and Technology ◽

10.32508/stdjet.v3i1.646 ◽

2020 ◽

Vol 3 (1) ◽

pp. First

Author(s):

Viết Cường Võ ◽

Phuong Hoang Nguyen ◽

Luan Le Duy Nguyen ◽

Van-Hung Pham

Keyword(s):

Power System ◽

Econometric Model ◽

Electricity Consumption ◽

Electricity Demand ◽

Master Plan ◽

Base Scenario ◽

Leading Position ◽

Long Term Forecasting ◽

High Scenario

An accurate forecasting for long-term electricity demand makes a major role in the planning of the power system in any country. Vietnam is one of the most economically developing countries in the world, and its electricity demand has been increased dramatically high of about 15%/y for the last three decades. Contribution of industry and construction sectors in GDP has been increasing year by year, and are currently holding the leading position of largest consumers with more than 50% sharing in national electricity consumption proportion. How to estimate the electricity consumption of these sectors correctly makes a crucial contribution to the planning of the power system. This paper applies an econometric model with Cobb Douglas production function - a top-down method to forecast electricity demand of the industry and construction sectors in Vietnam to 2030. Four variables used are the value of the sectors in GDP, income per person, the proportion of electricity consumption of the sectors in total, and electric price. Forecasted results show that the proposed method has a quite low MAPE of 7.66% for long-term forecasting. Variable of electric price does not affect the demand. This is a very critical result of the study for authority governors in Vietnam. In the base scenario of the GDP and the income per person, the forecasted electricity demands of the sectors are 112,853 GWh, 172,691 GWh, and 242,027 GWh in 2020, 2025, 2030, respectively. In high scenario one, the demands are 115,947 GWh, 181,591 GWh, and 257,272 GWh, respectively. The above values in the high scenario are less than from 9.0% to 15.8 % of that of the based on in the Revised version of master plan N0. VII.

Download Full-text

PRONÓSTICO EFICIENTE DE LA DEMANDA DIARIA DEL SISTEMA ELECTRICO INTERCONECTADO DEL PERU MEDIANTE ANALISIS ESTOCASTICO ARIMA CON SUCESOS EXTERNOS

Revista TECNIA ◽

10.21754/tecnia.v24i1.35 ◽

2017 ◽

Vol 24 (1) ◽

pp. 87

Author(s):

Salome Gonzales Chávez

Keyword(s):

Time Series ◽

Electricity Demand ◽

Demand Forecast ◽

External Effects ◽

Interconnected System ◽

Palabras Clave ◽

Day Of The Week ◽

The Mean ◽

External Events ◽

Central South

La demanda diaria del Sistema Eléctrico Interconectado Nacional-SEIN, posee características muy peculiares de tendencia, estacionalidad y aleatoriedad, situación que complica al proceso de estimación de su pronóstico. El objetivo del presente trabajo consiste en formular y calcular modelos ARIMA con Análisis de Sucesos Externos, a fin de lograr pronósticos eficientes de la demanda eléctrica de cada día siguiente, a nivel total y desagregado por áreas. Un buen pronóstico de la demanda diaria garantiza el despecho eficiente y económico de generación y transmisión, así como el aseguramiento y calidad de la demanda sectorial nacional. El enfoque metodológico lo constituye el tratamiento de cada serie temporal objetivo, mediante transformaciones estadístico-matemáticas apropiadas para alcanzar estabilidad tanto en varianzas como en medias regulares y estacionales; paralelamente filtrar los sucesos externos hasta alcanzar a un Modelo ARIMA predictivo de cada área del sistema eléctrico del Perú (Centro, Sur y Norte) y para cada día de la semana. Los resultados alcanzados en la presente investigación demuestran la eficiencia predictiva comparativa. Es decir, tomando como indicador de calidad de pronóstico al Error Absoluto Promedio Porcentual (MAPE), se han obtenido valores inferiores al 1% en las proyecciones de la demanda diaria total del SEIN, frente al 2% que se logra con actuales técnicas determinísticas. Palabras clave.- Pronóstico de Demanda, Despacho Eléctrico, ARIMA, Sucesos Externos, Serie Temporal, Proceso Estocástico, MAPE, Sistema Interconectado Nacional. ABSTRACTThe daily electric demand in Peruvian National Interconnected System-SEIN- has very particular trend, seasonality and characteristics external effects, a situation that complicates the process of estimating the short-term forecast. The aim of this paper is to formulate and calculate ARIMA models with External Events Analysis to achieve efficient forecasts of electricity demand each day, at total level and broken down by areas of the SEIN. The methodology is based on treating each time series using appropriate statistical-mathematical transformations to achieve stability in variance as regular seasonal averages, parallel external events to try to reach an optimal predictive model ARIMA each area of the electrical system of Peru (Central, South and North) and for each day of the week. The results demonstrate the predictive efficiency. Taking as a quality indicator forecast the Mean Absolute Percent Error (MAPE), have obtained values lower than 1% by the projections of the total daily demand SEIN versus 2% obtained with existing deterministic techniques. Keywords.- Demand Forecast, Electricity Demand, ARIMA, External Events, Time Series, Stochastic Process, MAPE, Electric Grid System.

Download Full-text