Load Forecasting using Time Series Techniques

2021 ◽  
pp. 11343-11357
Author(s):  
Shahida Khatoon, Ibraheem, Priti, Mohammad Shahid
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Trend ◽  
Moving Average ◽  
Load Forecasting ◽  
Electric Load ◽  
Time Data ◽  
Efficient Operation ◽  
Use Of Time ◽  
Distribution Company

Load Forecasting is of great significance for effective and efficient operation of power system. Use of time series is of much importance in load forecasting. In this study, effectiveness of different time series techniques is identified to gathered valuable information. The objective is to predict electric load efficiently and effectively. This paper analyses the prediction accuracy of variety of time series method in modeling Electric load forecasts. The study examines the time series forecasting methods applied to estimate future electric load, specifically, Moving Average (MA), Linear Trend, the Exponential and Parabolic Trend. A comparison of different forecasting techniques of Time Series is demonstrated on real time data. The data utilized for forecast is made available through a distribution company of India. The traditional linear models and hybrid models along with ANN are developed. These models are appraised for the forecasting capability.

scholarly journals Monitoring distributed computing beyond the traditional time-series histogram

2020 ◽  
Vol 245 ◽  
pp. 03036
Author(s):  
M S Doidge ◽  
P. A. Love ◽  
J Thornton
Keyword(s):  
Time Series ◽  
Distributed Computing ◽  
Real Time ◽  
A Priori ◽  
Time Data ◽  
Use Of Time ◽  
Computing Services ◽  
Monitoring Tools ◽  
Novel Approach ◽  
Current Monitoring

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.

scholarly journals Time Series Analysis of the Pollutants Imissions in Urban Areas

1970 ◽  
Vol 66 (2) ◽  
Author(s):  
Carmen Leane NICOLESCU ◽  
Daniel DUNEA ◽  
Virgil MOISE ◽  
Gabriel GORGHIU
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Urban Areas ◽  
Linear Trend ◽  
Moving Average ◽  
Quality Parameters ◽  
Temporal And Spatial Distribution ◽  
Simple Exponential Smoothing ◽  
Environmental Air ◽  
Local Agencies

Environmental pollution of urban areas is one of the key factors that local agencies and authorities have to consider in the decision-making process. To succeed a sustainable management of the environment, there is necessary to use different kinds of instruments in order to evaluate and forecast the evolution of the environmental state. Understanding temporal and spatial distribution of air quality is essential in making decisions for regional management. In this paper a model for urban air quality forecasting using time series of monthly averages concentrations is presented. Sedimentable dusts (SD), total suspended particulates (TSP), nitrogen dioxide (NO2), and sulfur dioxide (SO2), imissions, recorded between 1995 and 2008 in the urban area of Târgovişte city are used as inputs in the model. The measured pollutant data from the local Environmental Agency database were statistically analyzed in time series including monthly patterns using the auto-regressive integrated moving average (ARIMA) method, linear trend, simple moving average of three terms and simple exponential smoothing. There was discussed the efficiency of using this method in forecasting the environmental air quality. In general, ARIMA technique scores well in predicting the analysed environmental air quality parameters.

Electric Load Movement Forecasting Based on the DFT Interpolation with Periodic Extension

2015 ◽  
Vol 24 (08) ◽  
pp. 1550123
Author(s):  
Zong-Chang Yang
Keyword(s):  
Time Series ◽  
Load Forecasting ◽  
Arma Model ◽  
Ar Model ◽  
Electric Load ◽  
Ann Model ◽  
Method Performance ◽  
Bp Artificial Neural Network ◽  
Load Movement ◽  
Artificial Neural Network Ann

Electric load forecasting is increasingly important for the industry. This study addresses the load forecasting based on the discrete Fourier transform (DFT) interpolation. As the most common analysis method in the frequency domain, the conventional Fourier analysis cannot be directly applied to prediction. From the perspective of time-series analysis, electric load movement influenced by various factors is also a time-series, which is usually subject to cyclical variations. Then with periodic extension for the load movement, a forecasting approach based on the DFT interpolation is proposed for predicting its movement. The proposed DFT interpolation prediction model is applied to experiments of forecasting the daily EUNITE load movement and annual load movement of State Grid Corporation in China. The experimental results and analysis show potentiality of the proposed method. Performance comparisons indicate that the proposed DFT interpolation model performs better than the three commonly used interpolation algorithms as well as the classical autoregressive (AR) model, the ARMA model, and the BP-artificial neural network (ANN) model on the same forecasting tasks.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

Vector linear time series models

1976 ◽  
Vol 8 (2) ◽  
pp. 339-364 ◽  
Author(s):  
W. Dunsmuir ◽  
E. J. Hannan
Keyword(s):  
Time Series ◽  
Linear Models ◽  
Linear Time ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Multiple Time ◽  
Strong Law ◽  
Large Numbers ◽  
Spectral Approximations ◽  
Vector Time Series

This paper presents proofs of the strong law of large numbers and the central limit theorem for estimators of the parameters in quite general finite-parameter linear models for vector time series. The estimators are derived from a Gaussian likelihood (although Gaussianity is not assumed) and certain spectral approximations to this. An important example of finite-parameter models for multiple time series is the class of autoregressive moving-average (ARMA) models and a general treatment is given for this case. This includes a discussion of the problems associated with identification in such models.

scholarly journals Modelling New Zealand Road Deaths

2021 ◽  
Vol 32 (2) ◽  
pp. 4-15
Author(s):  
Colin Morrison ◽  
Ernest Albuquerque
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Time Data ◽  
Baseline Model ◽  
Explanatory Variables ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag

New Zealand is developing an integrated road safety intervention logic model. This paper describes a core component of this wider strategic research carried out in 2018: a baseline model that extrapolates New Zealand road deaths to 2025. The baseline will provide context to what Waka Kotahi NZ Transport Agency is trying to achieve. It offers a way of understanding what impact interventions have in acting with and against external influences affecting road deaths and serious trauma. The baseline model considers autonomous change at a macro level given social and economic factors that influence road deaths. Identifying and testing relationships and modelling these explanatory variables clarifies the effect of interventions. Time-series forecasting begins by carefully collecting and rigorously analysing sequences of discrete-time data, then developing an appropriate model to describe the inherent structure of the series. Successful time-series forecasting depends on fitting an appropriate model to the underlying time-series. Several time-series models were investigated in understanding road deaths in the New Zealand context. In the final modelling an autoregressive integrated moving average (ARIMA) model and two differing autoregressive distributed lag (ARDL) models were developed. A preferred model was identified. This ARDL model was used to project road deaths to 2025.

scholarly journals Technical note: Decomposing a time series into independent trend, seasonal and random components

2018 ◽  
Author(s):  
Dongqin Yin ◽  
Hannah Slatford ◽  
Michael L. Roderick
Keyword(s):  
Time Series ◽  
Traditional Approach ◽  
Linear Trend ◽  
Moving Average ◽  
Climatic Variability ◽  
Original Data ◽  
Technical Note ◽  
Decomposition Approach ◽  
Random Components ◽  
Original Time

Abstract. Many time series observations in hydrology and climate show large seasonal variations and it has long been common practice to separate the original data into trend, seasonal and random components. We were interested in using that decomposition approach as a basis for understanding variability in hydro-climatic time series. For that purpose, it is desirable that the trend, seasonal and random components are independent so that the variance of the original time series equals the sum of the variances of the three components. We show that the resulting decomposition with the trend component traditionally estimated either as a linear trend or a moving average does not produce components that are independent. Instead we introduce the rarely adopted two-way ANOVA model into studies of hydro-climatic variability and define the trend as equal to the annual anomaly. This traditional approach produces a decomposition with three independent components. We then use global land precipitation data to demonstrate a simple application showing how this decomposition method can be used as a basis for comparing hydro-climatic variability. We anticipate that the three-part decomposition based on the two-way ANOVA approach will prove useful for future applications that seek to understand the space-time dimensions of hydro-climatic variability.

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