APPLICATION OF SHORT-TERM FORECASTING MODELS FOR ENERGY ENTITY STOCK PRICE (STUDY ON INDIKA ENERGI TBK, JII)

This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation

Energies ◽

10.3390/en13010011 ◽

2019 ◽

Vol 13 (1) ◽

pp. 11 ◽

Cited By ~ 1

Author(s):

María Carmen Ruiz-Abellón ◽

Luis Alfredo Fernández-Jiménez ◽

Antonio Guillamón ◽

Alberto Falces ◽

Ana García-Garre ◽

...

Keyword(s):

Power Systems ◽

Demand Response ◽

Time Series Data ◽

Load Forecasting ◽

Real Data ◽

Series Data ◽

Renewable Generation ◽

Short Term ◽

Forecasting Models ◽

Short Term Forecasting

The development of Short-Term Forecasting Techniques has a great importance for power system scheduling and managing. Therefore, many recent research papers have dealt with the proposal of new forecasting models searching for higher efficiency and accuracy. Several kinds of artificial intelligence (AI) techniques have provided good performance at predicting and their efficiency mainly depends on the characteristics of the time series data under study. Load forecasting has been widely studied in recent decades and models providing mean absolute percentage errors (MAPEs) below 5% have been proposed. On the other hand, short-term generation forecasting models for photovoltaic plants have been more recently developed and the MAPEs are in general still far from those achieved from load forecasting models. The aim of this paper is to propose a methodology that could help power systems or aggregators to make up for the lack of accuracy of the current forecasting methods when predicting renewable energy generation. The proposed methodology is carried out in three consecutive steps: (1) short-term forecasting of energy consumption and renewable generation; (2) classification of daily pattern for the renewable generation data using Dynamic Time Warping; (3) application of Demand Response strategies using Physically Based Load Models. Real data from a small town in Spain were used to illustrate the performance and efficiency of the proposed procedure.

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The Ultra-Short-Term Forecasting of Global Horizonal Irradiance Based on Total Sky Images

Remote Sensing ◽

10.3390/rs12213671 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3671

Author(s):

Junxia Jiang ◽

Qingquan Lv ◽

Xiaoqing Gao

Keyword(s):

Back Propagation ◽

Back Propagation Neural Network ◽

Solar Irradiation ◽

Short Term ◽

Clear Sky ◽

Forecasting Models ◽

Forecasting Performance ◽

Sky Imager ◽

Short Term Forecasting ◽

Sky Conditions

Solar photovoltaics (PV) has advanced at an unprecedented rate and the global cumulative installed PV capacity is growing exponentially. However, the ability to forecast PV power remains a key technical challenge due to the variability and uncertainty of solar irradiance resulting from the changes of clouds. Ground-based remote sensing with high temporal and spatial resolution may have potential for solar irradiation forecasting, especially under cloudy conditions. To this end, we established two ultra-short-term forecasting models of global horizonal irradiance (GHI) using Ternary Linear Regression (TLR) and Back Propagation Neural Network (BPN), respectively, based on the observation of a ground-based sky imager (TSI-880, Total Sky Imager) and a radiometer at a PV plant in Dunhuang, China. Sky images taken every 1 min (minute) were processed to determine the distribution of clouds with different optical depths (thick, thin) for generating a two-dimensional cloud map. To obtain the forecasted cloud map, the Particle Image Velocity (PIV) method was applied to the two consecutive images and the cloud map was advected to the future. Further, different types of cloud fraction combined with clear sky index derived from the GHI of clear sky conditions were used as the inputs of the two forecasting models. Limited validation on 4 partly cloudy days showed that the average relative root mean square error (rRMSE) of the 4 days ranged from 5% to 36% based on the TLR model and ranged from 12% to 32% based on the BPN model. The forecasting performance of the BPN model was better than the TLR model and the forecasting errors increased with the increase in lead time.

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