Short-term Forecasting of the Abu Dhabi Electricity Load Using Multiple Weather Variables

Friedrich, Luiz; Afshari, Afshin

doi:10.1016/j.egypro.2015.07.616

Cited by 62 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work different methods are compared and finding better algorithm to analyses result at each phase/step. Prakash GL et al [13] in this research work presents three different models with Artificial Neural Networks that are used to predict both days ahead and hour's basis electricity load. The past historical electricity load data as well as the past historical weather forecasted data were collected and then by using clustering techniques the cleansing the data is preformed.…”

Section: Neural Networkmentioning

confidence: 99%

Various Electricity Load Forecasting Techniques with Pros and Cons

Singh¹,

Maini²

2020

IJRTE

View full text Add to dashboard Cite

The rapid growth of stored information in the demand forecasting, associated with data analysis provoked an utmost need for generating a powerful tool which must be capable of extracting hidden and vital knowledge of load forecasting from available vast data sets. Being a promising sub domain of computer science, numerous data mining techniques suits the solution to this problem very well. This paper presents a vast, rigorous and comparable survey of tremendous data mining techniques useful in forecasting the electricity load demand of different geographic area. Based upon the rigorous survey, primary challenges involved in the current technologies and future goals are also discussed.

show abstract

Section: Neural Networkmentioning

confidence: 99%

Various Electricity Load Forecasting Techniques with Pros and Cons

Singh¹,

Maini²

2020

IJRTE

View full text Add to dashboard Cite

show abstract

“…Satish et al [41] studied the effect of temperature with 20 training patterns and compared the results, but there is no comprehensive analysis with atmospheric variables. Friedrich and Afshari [42] investigated the results for Abu Dhabi city electricity load using multiple weather variable for a 24 h-48 h prediction horizon and obtained a very promising result of 1.5% MAPE. Apadula et al [1] analyzed weather and calendar variables affecting monthly electricity demand using an MLR model for Italy and concluded that the calendar component considerably contributed from January-May with 4-7.7%.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables

Chapagain

2018

Energies

View full text Add to dashboard Cite

The quality of short-term electricity demand forecasting is essential for the energy market players for operation and trading activities. Electricity demand is significantly affected by non-linear factors, such as climatic conditions, calendar components and seasonal behavior, which have been widely reported in the literature. This paper considers parsimonious forecasting models to explain the importance of atmospheric variables for hourly electricity demand forecasting. Many researchers include temperature as a major weather component. If temperature is included in a model, other weather components, such as relative humidity and wind speed, are considered as less effective. However, several papers mention that there is a significant impact of atmospheric variables on electricity demand. Therefore, the main purpose of this study is to investigate the impact of the following atmospheric variables: rainfall, relative humidity, wind speed, solar radiation, and cloud cover to improve the forecasting accuracy. We construct three different multiple linear models (Model A, Model B, and Model C) including the auto-regressive moving average with exogenous variables (ARMAX) with the mentioned exogenous weather variables to compare the performances for Hokkaido Prefecture, Japan. The Bayesian approach is applied to estimate the weight of each variable with Gibbs sampling to approximate the estimation of the coefficients. The overall mean absolute percentage error (MAPE) performances of Model A, Model B, and Model C are estimated as 2.43%, 1.98% and 1.72%, respectively. This means that the accuracy is improved by 13.4% by including rainfall, snowfall, solar radiation, wind speed, relative humidity, and cloud cover data. The results of the statistical test indicate that these atmospheric variables and the improvement in accuracy are statistically significant in most of the hours. More specifically, they are significant during highly fluctuating and peak hours.

show abstract

“…In [4], neural networks with multilayer perceptrons are proposed, to forecast the natural gas consumption in Szczecin, Poland. Fuzzy approaches are proposed in [5] [6] and autoregressive integrated moving average (ARIMA) algorithms in [7].…”

Section: Introductionmentioning

confidence: 99%