AI-based Short-Term Electric Time Series Forecasting

In current scenario of various electrical profiles like load profile, energy met profile, peak demand, etc. are very complex and therefore affected proper power system planning. Electrical forecasting is an important part in proper power system planning. Classical models, i.e., time series models and other conventional models are restricted for linear and stationary electrical profiles. Consequently, these models are not suitable for accurate electrical forecasting. In this paper, artificial neural network (ANN) based forecasting models are proposed to forecast hydro generation, energy met and peak demand. Auto-regressive (AR), moving average (MA), Auto-regressive Moving average (ARMA) and auto-regressive integrated moving average (ARIMA) are also developed to show the effectiveness of ANN based models over time series models. Additionally, best selection of hidden neurons in ANN is also shown here.

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A Deep Learning Approach to Forecasting Monthly Demand for Residential–Sector Electricity

Sustainability ◽

10.3390/su12083103 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3103 ◽

Cited By ~ 4

Author(s):

Hyojoo Son ◽

Changwan Kim

Keyword(s):

Time Series ◽

Deep Learning ◽

Power System ◽

Electricity Demand ◽

Efficient Production ◽

Power System Planning ◽

System Planning ◽

Residential Sector ◽

Use Of Resources ◽

Proposed Model

Forecasting electricity demand at the regional or national level is a key procedural element of power-system planning. However, achieving such objectives in the residential sector, the primary driver of peak demand, is challenging given this sector’s pattern of constantly fluctuating and gradually increasing energy usage. Although deep learning algorithms have recently yielded promising results in various time series analyses, their potential applicability to forecasting monthly residential electricity demand has yet to be fully explored. As such, this study proposed a forecasting model with social and weather-related variables by introducing long short-term memory (LSTM), which has been known to be powerful among deep learning-based approaches for time series forecasting. The validation of the proposed model was performed using a set of data spanning 22 years in South Korea. The resulting forecasting performance was evaluated on the basis of six performance measures. Further, this model’s performance was subjected to a comparison with the performance of four benchmark models. The performance of the proposed model was exceptional according to all of the measures employed. This model can facilitate improved decision-making regarding power-system planning by accurately forecasting the electricity demands of the residential sector, thereby contributing to the efficient production and use of resources.

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Multi-Year High-Voltage Power System Planning Considering Active Power Curtailment

Energies ◽

10.3390/en13184920 ◽

2020 ◽

Vol 13 (18) ◽

pp. 4920

Author(s):

Florian Schäfer ◽

Martin Braun

Keyword(s):

Time Series ◽

Power System ◽

High Voltage ◽

Renewable Energy Sources ◽

Active Power ◽

Case Method ◽

Power System Planning ◽

System Planning ◽

Worst Case ◽

Planning Methods

Integrating active power curtailment (APC) of renewable energy sources (RES) in power system planning reduces necessary investments in the power system infrastructure. In current target grid planning methods, APC is considered by fixed curtailment factors without considering the provided flexibility to its full extent. Time-series-based planning methods allow the integration of the time dependency of RES and loads in power system planning, leading to substantial cost savings compared to the worst-case method. In this paper, we present a multi-year planning strategy for high-voltage power system planning, considering APC as an alternative investment option to conventional planning measures. A decomposed approach is chosen to consider APC and conventional measures in a long-term planning horizon of several years. The optimal investment path is obtained with the discounted cash flow method. A case study is conducted for the SimBench high-voltage urban benchmark system. Results show that the time-series-based method allows for reducing investments by up to 84% in comparison to the worst-case method. Furthermore, a sensitivity analysis shows the variation in total expenditures with changing cost assumptions.

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Quantitative Evaluation Method of Each Generation Margin for Power System Planning

IEEJ Transactions on Power and Energy ◽

10.1541/ieejpes.129.533 ◽

2009 ◽

Vol 129 (4) ◽

pp. 533-540 ◽

Cited By ~ 1

Author(s):

Su Su ◽

Kazuyuki Tanaka

Keyword(s):

Power System ◽

Quantitative Evaluation ◽

Evaluation Method ◽

Power System Planning ◽

System Planning

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A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.6760 ◽

2020 ◽

Vol 5 (1) ◽

pp. 374

Author(s):

Pauline Jin Wee Mah ◽

Nur Nadhirah Nanyan

Keyword(s):

Time Series ◽

Palm Oil ◽

Moving Average ◽

Forecast Accuracy ◽

Peninsular Malaysia ◽

Time Series Models ◽

Crude Palm Oil ◽

Univariate Time Series ◽

Import And Export ◽

Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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Bridging granularity gaps to decarbonize large‐scale energy systems—The case of power system planning

Energy Science & Engineering ◽

10.1002/ese3.891 ◽

2021 ◽

Author(s):

Karl‐Kiên Cao ◽

Jannik Haas ◽

Evelyn Sperber ◽

Shima Sasanpour ◽

Seyedfarzad Sarfarazi ◽

...

Keyword(s):

Power System ◽

Large Scale ◽

Energy Systems ◽

Power System Planning ◽

System Planning

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System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

Engineering Proceedings ◽

10.3390/engproc2021005046 ◽

2021 ◽

Vol 5 (1) ◽

pp. 46

Author(s):

Mostafa Abotaleb ◽

Tatiana Makarovskikh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Time Series ◽

Time Series Models ◽

Third Wave ◽

The Third ◽

Modeling And Forecasting ◽

Auto Regressive ◽

The Russian Federation ◽

R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

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