ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise (original) (raw)
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Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Energy, 2008
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts.
FORECASTING ELECTRICITY CONSUMPTION USING AUTO-REGRESSIVE INTEGRATED MOVING AVERAGE
Science International-Lahore, 2021
Electricity plays a vital role in this modern world. It is considered the backbone of an economy's prosperity and progress. The availability of a huge amount of energy has resulted in a shorter working day, higher agricultural and industrial production, and have better transportation facilities. Modern society is so much dependent upon the use of electrical energy that it has become part of our life. It forms a key part of human everyday life. As the economy grows, demand for energy increases. Cagayan de Oro City's growing population and developing technology have demanded increasing consumption of electrical energy to provide light, heat, and power for the countless machines that are essential to the life and work of the residents. Usage will continue to increase because of rising living standards but higher costs may act to reduce the rate of rising. With the premise, there is a need to predict the future electrical power consumption to help CEPALCO to be prepared and for them to provide sufficient electrical energy for Cagayan de Oro City. Statistical forecasting is a method that uses the past to predict the future by identifying trends and patterns within the data to develop a forecast. The study deals with the application of autoregressive integrated moving average (ARIMA) for electrical power consumption in Cagayan de Oro City. After obtaining the results of ARIMA, it shows that ARIMA accumulates less absolute percentage error. The reason why ARIMA performed well is that the nature or behavior of the data that have been gathered contains linear trends although the data are fluctuating still the behavior is increasing which implies that the data of the monthly electricity consumption are more likely linear. Hence, the more the behavior of the data becomes linear, the more ARIMA is effective in forecasting.
Modeling and forecasting electricity loads: A comparison
Econometrics, 2005
In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components -a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed seasonal volatility technique. In both models the stochastic component is described by an ARMA time series. Models are tested on a time series of system-wide loads from the California power market and compared with the official forecast of the California System Operator (CAISO).
ARIMA with Regression Model in Modelling Electricity Load Demand
Journal of Telecommunication, Electronic and Computer Engineering, 2016
Electricity is among the most crucial needs for every people in this world. It is defined by the set of physical phenomena related with the flow of electrical charge. The importance of electricity itself leads to the increasing electricity load demand in the world including Malaysia. The purpose of the current study is to evaluate the performance of combined ARIMA with Regression model in forecasting electricity load demand in Johor Bahru. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) and Regression models will be used as benchmark models since the model has been proven in many forecasting context. Using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) as a forecasting accuracy criteria, the study concludes that the combined method is more appropriate model.
Load Forecasting using Autoregressive Integrated Moving Average and Artificial Neural Network
International Journal of Advanced Computer Science and Applications, 2018
Electric load forecasting is a challenging research problem due to the complicated nature of its dataset involving both linear and nonlinear properties. Various literatures attempted to develop forecasting models that utilized statistical in combination with machine learning approaches deal with the dataset's linear and nonlinear components to obtain close to accurate predictions. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as forecasting models for a power utility's dataset in order to predict day-ahead electric load. Electric load data preparation, models implementation and forecasting evaluation was conducted to assess if the prediction of the models met the acceptable error tolerance for day-ahead electric load forecasting. A Java-based system made use of R Statistical Software implemented ARIMA(8,1,2) while Encog Library was used to implement the ANN model composing of Resilient Propagation as the training algorithm and Hyperbolic Tangent as the activation function. The ANN+ARIMA hybrid model was found out to deliver a Mean Absolute Percentage Error (MAPE) of 4.09% which proves to be a viable technique in electric load forecasting while showing better forecasting results than solely using ARIMA and ANN. Through this research, both statistical and machine learning approaches were implemented as a forecasting model combination to solve the linear and non-linear properties of electric load data.
International Journal of Advanced Computer Science and Applications, 2019
The complementary strengths and weaknesses of both statistical modeling paired with machine learning has been an ongoing technique in the development and implementation of forecasting models that analyze the dataset's linear as well as nonlinear components in the generation of accurate prediction results. In this paper, autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) were implemented as a hybrid forecasting model for a power utility's dataset in order to predict the next day's electric load consumption. ARIMA and ANN models were serially developed resulting to the findings that out of the twelve evaluated ARIMA models, ARIMA (8,1,2) exhibited the best forecasting performance. After identifying the optimal ANN layers and input neurons, this study showed that out of the six evaluated supervised feedforward ANN models, the ANN model which employed Hyperbolic Tangent activation function and Resilient Propagation training algorithm also exhibited the best forecasting performance. With Zhang's ARIMA and ANN hybridization technique, this study showed that the hybrid model delivered Mean Absolute Percentage Error (MAPE) of 4.09% which is within the 5% internationally accepted forecasting error for electric load forecasting. Through the findings of this research, both the ARIMA statistical model and ANN machine learning approaches showed promising results in being implemented as a forecasting model pair to analyze the linear as well as non-linear properties of a power utility's electric load data.
Electricity Demand Estimation Using ARIMA Forecasting Model
Recent Developments in Electronics and Communication Systems, 2023
The aim of this study is to estimate the future electricity demand for domestic and commercial purpose. With the rising demand for power at households and industrial levels, it is more critical and important than ever to estimate future electricity needs so that demands in future can be met. In this paper, the ARIMA forecasting model with machine leaning techniques is presented for electricity demands forecasting. Time series decomposition is used to understand and split the data into test and train. ARIMA model is also compared to some similar models and benefits of using ARIMA model are also discussed. The results of this study show that ARIMA model can be used for forecasting electricity demand with lesser train and test error values as 0.10 and 0.04 respectively.
A Statistical Data-Filtering Method Proposed for Short-Term Load Forecasting Models
Journal of Electrical Engineering & Technology, 2020
Reliability assessment of the SCADA-system based load data is necessary for improving accuracy of short-term load forecasting (STLF) methods in a distribution network (DN). Specifically, the reliability evaluation of the load data is to properly eliminate noise/outliers caused by random power consumption behaviors or the sudden change in load demand from industrial and residential customers in the DN. Thus, this paper proposes a novel statistical data-filtering method, working at an input data pre-processing stage, which will evaluate the reliability of input load data by analyzing all possible data confidence levels in order to filter-out the noise/outliers for accuracy improvement of different short-term load forecasting models. The proposed statistical data-filtering method is also compared to other existing data-filtering methods (such as Kalman Filter, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Discrete Wavelet Transform (DWT) and Singular Spectrum Analysis (SSA)). Moreover, several case studies of short-term load forecasting for a typical 22 kV distribution network in Vietnam are conducted with an Artificial Neural Network (ANN) model, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model, a combined model of Long Short-Term Memory Network and Convolutional Neural Network (LSTM-CNN), and a conventional Autoregressive Integrated Moving Average (ARIMA) model to validate the statistical data-filtering method proposed. The achieved results demonstrate which the STLF using ANN, LSTM-RNN, LSTM-CNN, and ARIMA models with the statistical data-filtering method can all outperform those with the existing data-filtering methods. Additionally, the numerical results also indicate that in case the SCADA-based load data is normally distributed, time-series forecasting models should be more preferred than neural network models; otherwise, when the SCADA-based load data contains multiple normally distributed sub-datasets, neural network-based prediction models are highly recommended.
IJERT-Short-Term Load Forecasting using Statistical Methods: A Case Study on Load Data
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/short-term-load-forecasting-using-statistical-methods-a-case-study-on-load-data https://www.ijert.org/research/short-term-load-forecasting-using-statistical-methods-a-case-study-on-load-data-IJERTV9IS080182.pdf This paper presents the study of Moving Averages (MA), Autoregressive Moving Averages (ARMA) and Kalman Filter (KF) techniques for load forecasting. The data considered was Andhra Pradesh State electricity demand (MW) at every 15 minutes of 18 th May 2014. For the time series data both the methods ARMA and Kalman Filter techniques are used to predict and forecast the load. The results indicated that Kalman Filter gives better load forecasting as compared to ARMA in terms of less measurement of error using Mean Absolute Percentage Error (MAPE).
Comparison of Short-Term Load Forecasting Based on Kalimantan Data
Indonesian Journal of Statistics and Its Applications, 2021
This paper investigates a case study on short term forecasting for East Kalimantan, with emphasis on special days, such as public holidays. A time series of load demand electricity recorded at hourly intervals contains more than one seasonal pattern. There is a great attraction in using a modelling time series method that is able to capture triple seasonalities. The Triple SARIMA model has been adapted for this purpose and competitive for modelling load. Using the least squares method to estimate the coefficients in a triple SARIMA model, followed by model building, model assumptions and comparing model criteria, we propose and demonstration the triple Seasonal Autoregressive Integrated Moving Average model with AIC 290631.9 and SBC 290674.2 as the best model for this study. The Triple seasonal ARIMA is one of the alternative strategy to propose accurate forecasts of electricity load Kalimantan data for planning, operation maintenance and market related activities.