Electrical Load Forecasting: A methodological overview

Rostum, Medhat Abd el Azem El Sayed; Zamel, Amr; Moustafa, Hassan; Ziedan, Ibrahim

doi:10.14419/ijet.v9i3.30706

Cited by 5 publications

(3 citation statements)

References 80 publications

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“…Each of these clusters is made up of data that has a high inter-similarity but a low intra-similarity. There are several types of clustering approaches, including partitioning methods, hierarchical methods, density-based methods, gridbased methods, model-based methods, and constraint-based methods [7]. Clustering is used to categorize electricity usage into three groups: highest, medium, and lowest.…”

Section: Section 1: Data Gathering and Clusteringmentioning

confidence: 99%

Load Profile Forecasting using a Time Series Model for Solar Rooftop and Integrated Carpark of a Public University in Malaysia

Nurfadzilah Ahmad

2024

ARFMTS

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A precise and accurate load prediction is critical in a developing country like Malaysia to save the energy consumption. Power producers use load profile data and statistics to analyze and forecast the quantity of electricity required to be available at a given time. Load forecasting is the process of predicting future load requirements and this research focuses on the short-term load forecasting (STLF) for solar rooftop and integrated car park built at one of the public universities in Malaysia. Power system planners and demand controllers must ensure that there is enough generation to meet the increased demand. Load forecasting models that are accurate can lead to better budgeting, maintenance scheduling, and fuel management. This project seeks to anticipate the highest demand of power utilized by the consumer based on prior load profiles using a Multiple Regression Time Series model developed using MATLAB software. It consists of the following steps: data collection, clustering, series transformation using differentiation transform to remove trend and seasonal structure from the dataset, model identification using autocorrelation function (ACF) and partial correlation function (PCF), model estimation using ARIMA time series errors and maximum likelihood probability, and finally model forecasting using Auto Regression (AR), Moving Average. In this case, the goal is to ensure the power production equals electricity demand, and achieving the target will assure energy security, dependability, and the capacity to maximize profits while minimizing losses.

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Section: Section 1: Data Gathering and Clusteringmentioning

confidence: 99%

Load Profile Forecasting using a Time Series Model for Solar Rooftop and Integrated Carpark of a Public University in Malaysia

Nurfadzilah Ahmad

2024

ARFMTS

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“…Bholanath et.al., have been analysed the time series data by using ARIMA models [10]. Medhat Rostum et.al., have used various stochastic forecasting techniques to forecast demand of electric load and compared them by measures of performances [11]. Cheng-Ming Lee and Chia-Nan Ko have predicted the short term electric load data by using ARIMA-model [12].…”

Section: Related Workmentioning

confidence: 99%

A Predictive Analytic Time Series Forecasting Model

Pagadala Srivyshnavi

2024

jes

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Predictive Analytics is the process of using predictive modelling techniques to analyse the various types of data in agriculture, business management, engineering, weather forecasting, planning, meteorology etc. Most frequently using predictive modelling technique in Electrical load forecasting and Agricultural production forecasting is the time series forecasting technique. Particularly, in the electrical systems, the future electric load demand and peak load can be predicted by using several methods such as regression methods and time series methods; data science methods namely ML, DL, SVM, ANN, AI techniques etc. In the empirical study, a time series forecasting technique based on a selected ARIMA (1,1,2) model has been applied to an agricultural big data and obtained forecasts for Bengal gram average monthly prices of Andhra Pradesh state, India from Jan’2023 to Dec’2025. In the similar lines, the proposed time series forecasting modelling can be applied to forecast short term electric load demand by using the given previous bigdata.

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“…Because of the importance of an accurate prediction for the reliable operation and planning of power system, there are several researches on LF. LF process can be classified according to the prediction horizon into four categories (Hernandez et al , 2014; Rostum et al , 2020): very short-term load forecasting (VSTLF) ranging from several minutes to few hours ahead; STLF ranging from days to few weeks ahead; medium-term load forecasting ranging from months to one year ahead; and long-term load forecasting ranging from one year up to several years ahead. In specific, the forecasting of smart meter electricity consumption is addressed in this paper.…”

Section: Literature Reviewmentioning

confidence: 99%

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

Rostum

Moustafa

Ziedan

et al. 2021

COMPEL

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Purpose The current challenge for forecasting smart meters electricity consumption lies in the uncertainty and volatility of load profiles. Moreover, forecasting the electricity consumption for all the meters requires an enormous amount of time. Most papers tend to avoid such complexity by forecasting the electricity consumption at an aggregated level. This paper aims to forecast the electricity consumption for all smart meters at an individual level. This paper, for the first time, takes into account the computational time for training and forecasting the electricity consumption of all the meters. Design/methodology/approach A novel hybrid autoregressive-statistical equations idea model with the help of clustering and whale optimization algorithm (ARSEI-WOA) is proposed in this paper to forecast the electricity consumption of all the meters with best performance in terms of computational time and prediction accuracy. Findings The proposed model was tested using realistic Irish smart meters energy data and its performance was compared with nine regression methods including: autoregressive integrated moving average, partial least squares regression, conditional inference tree, M5 rule-based model, k-nearest neighbor, multilayer perceptron, RandomForest, RPART and support vector regression. Results have proved that ARSEI-WOA is an efficient model that is able to achieve an accurate prediction with low computational time. Originality/value This paper presents a new hybrid ARSEI model to perform smart meters load forecasting at an individual level instead of an aggregated one. With the help of clustering technique, similar meters are grouped into a few clusters from which reduce the computational time of the training and forecasting process. In addition, WOA improves the prediction accuracy of each meter by finding an optimal factor between the average electricity consumption values of each cluster and the electricity consumption values for each one of its meters.

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Electrical Load Forecasting: A methodological overview

Cited by 5 publications

References 80 publications

Load Profile Forecasting using a Time Series Model for Solar Rooftop and Integrated Carpark of a Public University in Malaysia

Load Profile Forecasting using a Time Series Model for Solar Rooftop and Integrated Carpark of a Public University in Malaysia

A Predictive Analytic Time Series Forecasting Model

A combined effective time series model based on clustering and whale optimization algorithm for forecasting smart meters electricity consumption

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