Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models (original) (raw)
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Electricity demand load forecasting of the Hellenic power system using an ARMA model
Electric Power Systems Research, 2010
Effective modeling and forecasting requires the efficient use of the information contained in the available data so that essential data properties can be extracted and projected into the future. As far as electricity demand load forecasting is concerned time series analysis has the advantage of being statistically adaptive to data characteristics compared to econometric methods which quite often are subject to errors and uncertainties in model specification and knowledge of causal variables. This paper presents a new method for electricity demand load forecasting using the multi-model partitioning theory and compares its performance with three other well established time series analysis techniques namely Corrected Akaike Information Criterion (AICC), Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The suitability of the proposed method is illustrated through an application to actual electricity demand load of the Hellenic power system, proving the reliability and the effectiveness of the method and making clear its usefulness in the studies that concern electricity consumption and electricity prices forecasts.
Egypt has faced a major problem in balancing electricity produced and electricity consumed at any time in the day. Therefore, short-term forecasts are required for controlling and scheduling of electric power system. Electricity demand series has more than one seasonal pattern. Double seasonality of the electricity demand series in many countries have considered. Double seasonality pattern of Egyptian electricity demand has not been investigated before. For the first time, different double seasonal autoregressive integrated moving average (DSARIMA) models are estimated for forecasting Egyptian electricity demand using maximum likelihood method. í µí±«í µí±ºí µí±¨í µí±¹í µí±°í µí±´í µí±¨(µí±¨(í µí¿, í µí¿, í µí¿) (í µí¿ , í µí¿, í µí¿) í µí¿í µí¿ (í µí¿ , í µí¿ , í µí¿) í µí¿í µí¿í µí¿ model is selected based on Schwartz Bayesian Criterion (SBC). In addition, empirical results indicated the accuracy of the forecasts produced by this model for different time horizon.
Adaptive load forecasting of the Hellenic electric grid
Journal of Zhejiang University Science, 2008
Designers are required to plan for future expansion and also to estimate the grid’s future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid’s utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal behavior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.
ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise
Energies, 2021
The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They c...
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.
Energy Efficiency, 2015
The main purpose of the present study is to develop a simple yet proper top-down model for forecasting the energy demand of the residential and commercial sectors in Iran. This model can be used as a tool of scenario analysis to predict the emerging energy demand in future. The proposed model would be systematically developed and selected based on various quantified exogenous variables. For this purpose, a certain model out of a collection of 41,472 parallel models with different inputs and dynamics is chosen as the most appropriate model. According to the logical conjunctive relationships between the variables, the structure of all competing models is established to log-linear. Different possible combinations of various measures for the exogenous variables generate parallel models. Then, an automated fuzzy decision-making (FDM) process determines the best model. Finally, defining several scenarios, the energy demand of the residential and commercial sectors in Iran for the period of 2013 to 2021 is forecasted. The results showed that despite of de-subsidization, which is included by a dummy variable, the energy demand will grow by an average rate of about 3 % annually.
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).
International Journal of Modelling, Identification and Control, 2008
In this paper, a study on how to perform simultaneous order and parameter estimation of multivariate (MV) ARMA (autoregressive moving average) models under the presence of noise is addressed. The proposed method, which is computationally efficient, is an extension of a previously presented method for MV AR models and is based on the well established and widely applied multi-model partitioning theory. A series of computer simulations indicate that the method is infallible in selecting the correct model order in very few steps. The simultaneous estimation of the multivariate ARMA parameters is also another benefit of the proposed method. The results are compared with two other established order selection criteria namely Akaike's Information Crieterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). Finally, it is shown that the method is also successful in tracking model order changes, in real time.
2019
The main aim of this paper is to emphasize on the significant role of data pre-processing phase in improving the short-term load demand forecasting. Different transformation approaches including normalization, Zscore and Box-Cox method are applied and various input selection methods including forward selection, backward selection, stepwise regression and principle component analysis are used to see how the combination of these pre-processing techniques will influence the performance of different parametric (ARIMA, ARIMAX, MLR) and non-parametric (NAR, NARX, SVR, ANFIS) predictors. The data was collected from the daily load demand of Ottawa, Canada. It was observed that the Box-Cox transformation significantly improved the performance of all predictors and the findings demonstrated the superior role of exogenous variables in accuracy improvement of all predictors. In terms of MAPE, the value of 2.27% for ARIMA model improved to 1.75% with ARIMAX using temperature, and it decreased fr...
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.