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

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 11 ◽  
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.

scholarly journals STUDI PERAMALAN BEBAN RATA – RATA JANGKA PENDEK MENGGUNAKAN METODA AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA

Sutet ◽  
2018 ◽  
Vol 7 (2) ◽  
pp. 93-101
Author(s):  
Redaksi Tim Jurnal
Keyword(s):  
Power Plants ◽  
Time Series Data ◽  
Moving Average ◽  
Current Model ◽  
Series Data ◽  
Electrical Load ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variable ◽  
Short Term Forecasting

Forecasting. Plans, power plants ,. Electricity needs are increasingly changing daily, so the State Electricity Company (PLN) as a provider of energy must be able to predict daily electricity needs. Short-term forecasting is the prediction of electricity demand for a certain period of time ranging from a few minutes to a week ahead. in shortterm electrical forecasting much of the literature describes the techniques and methods applied in forecasting, Autoregresive Integrated Moving Average (ARIMA), linear regression, and artificial intelligence such as Artificial Neural Networks and fuzzy logic. Short-term forecasting will be done by the authors using time series data that is the data of the use of electric power daily (electrical load) and ARIMA as a method of forecasting. ARIMA method or often called Box-Jenkins technique to find this method is suitable to predict variable costs quickly, simply, and cheaply because it only requires data variables to be predicted. ARIMA can only be used for short-term forecasting. ARIMA is a special linear test, in the form of forecasting this model is completely independent variable variables because this model uses the current model and past values of the dependent variable to produce an accurate short-term forecast.

scholarly journals Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

2022 ◽  
Vol 18 (2) ◽  
pp. 198-223
Author(s):  
Farin Cyntiya Garini ◽  
Warosatul Anbiya
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Short Term ◽  
Seasonal Factors ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Over Time

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

FlashP

2021 ◽  
Vol 14 (5) ◽  
pp. 721-729
Author(s):  
Shuyuan Yan ◽  
Bolin Ding ◽  
Wei Guo ◽  
Jingren Zhou ◽  
Zhewei Wei ◽  
...  
Keyword(s):  
Real Time ◽  
Time Series Data ◽  
Real Data ◽  
Series Data ◽  
High Dimensional ◽  
Forecasting Model ◽  
Business Decisions ◽  
Forecasting Models ◽  
Sampling Algorithms ◽  
Forecasting System

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution its alternatives on real data.

scholarly journals Development and Evaluation of Bidirectional LSTM Freeway Traffic Forecasting Models Using Simulation Data

2021 ◽  
Author(s):  
Rusul L. Abduljabbar ◽  
Hussein Dia ◽  
Pei-Wei Tsai
Keyword(s):  
Simulation Model ◽  
Time Series Data ◽  
Short Term Memory ◽  
Superior Performance ◽  
Series Data ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Forecasting Models ◽  
Multiple Prediction

Abstract Long short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 minutes into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25-100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

scholarly journals Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rusul L. Abduljabbar ◽  
Hussein Dia ◽  
Pei-Wei Tsai
Keyword(s):  
Simulation Model ◽  
Time Series Data ◽  
Short Term Memory ◽  
Superior Performance ◽  
Series Data ◽  
Short Term ◽  
Traffic Demand ◽  
Traffic Forecasting ◽  
Forecasting Models ◽  
Multiple Prediction

AbstractLong short-term memory (LSTM) models provide high predictive performance through their ability to recognize longer sequences of time series data. More recently, bidirectional deep learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia. The simulation model was extensively calibrated and validated to a high degree of accuracy using field data collected from 55 detectors on the freeway. The base year simulation model was then used to generate loop detector data including speed, flow and occupancy which were used to develop and compare a number of LSTM models for short-term traffic prediction up to 60 min into the future. The modelling results showed that BiLSTM outperformed other predictive models for multiple prediction horizons for base year conditions. The simulation model was then adapted for future year scenarios where the traffic demand was increased by 25–100 percent to reflect potential future increases in traffic demands. The results showed superior performance of BiLSTM for multiple prediction horizons for all traffic variables.

scholarly journals Comparison Analysis of Facebook’s Prophet, Amazon’s DeepAR+ and CNN-QR Algorithms for Successful Real-World Sales Forecasting

2021 ◽  
Vol 13 (2) ◽  
pp. 67-84
Author(s):  
Emir Žunić ◽  
Kemal Korjenić ◽  
Sead Delalić ◽  
Zlatko Šubara
Keyword(s):  
Time Series ◽  
Real World ◽  
Time Series Data ◽  
Real Data ◽  
Detailed Comparison ◽  
Series Data ◽  
Comparison Analysis ◽  
Forecasting Models ◽  
The Creation ◽  
Analysis Of Time Series

By successfully solving the problem of forecasting, the processes in the work of various companies are optimized and savings are achieved. In this process, the analysis of time series data is of particular importance. Since the creation of Facebook’s Prophet, and Amazon’s DeepAR+ and CNN-QR forecasting models, algorithms have attracted a great deal of attention. The paper presents the application and comparison of the above algorithms for sales forecasting in distribution companies. A detailed comparison of the performance of algorithms over real data with different lengths of sales history was made. The results show that Prophet gives better results for items with a longer history and frequent sales, while Amazon’s algorithms show superiority for items without a long history and items that are rarely sold.

scholarly journals Short-Term Forecasting Model of Animal Food Commodities in Central Sulawesi

2020 ◽  
Vol 8 (1) ◽  
pp. 17
Author(s):  
Rustam Abdul Rauf ◽  
Dian Safitri ◽  
Christoporus Christoporus ◽  
Effendy Effendy ◽  
Muhardi Muhardi
Keyword(s):  
Time Series Data ◽  
Supply And Demand ◽  
Series Data ◽  
Forecasting Model ◽  
Short Term ◽  
Broiler Meat ◽  
Animal Food ◽  
Short Term Forecasting ◽  
Food Commodities ◽  
Central Sulawesi

Shifting patterns of community consumption from vegetable protein to animal protein encouraged high demand for animal food, so it was needed an estimate of the supply and demand for its products. Therefore, this research aimed to analyze the short-term forecasting model of the production and price of beef and broiler meat in Central Sulawesi. The research used time series data. Production data and price of beef and broiler meat were taken from 2015 - 2019. The analytical tool used was the ARIMA Box-Janskin forecasting method. The results showed a short-term forecasting model for beef production (1,0,0) and broiler meat (3,2,1). Short-term forecasting model for beef price (1,0,1) and broiler meat (1,1,1).  This finding could be used as a reference in making policies related to the production and price of beef and broilers meat in order to meet the needs of the community, especially in Central Sulawesi.

scholarly journals Forecasting short-term carbon emission futures price volatility: information for hedging carbon emission futures risk

2017 ◽  
Vol 8 (4) ◽  
pp. 6-13 ◽  
Author(s):  
Collins C. Ngwakwe
Keyword(s):  
Environmental Economics ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Time Series Data ◽  
Price Volatility ◽  
Series Data ◽  
Carbon Markets ◽  
Short Term ◽  
Futures Price ◽  
Short Term Forecasting

This paper aimed to illustrate how short-term carbon futures speculators might use short-term carbon emission futures data to predict and forecast carbon prices. The paper became apposite given ubiquitous research focussing on long-term carbon futures data, which has left out short-term carbon emission futures speculators with information. Therefore, this paper demonstrated that short-term speculators in carbon futures could indeed use short-term time series data on carbon futures to make a reliable prediction and forecasting of carbon emissions futures price volatility within a short term and thus decide on investment opportunity. The sample data results showed that short-term data could produce a dependable in-sample futures prediction since the in-sample prediction fell within the 95% confidence interval. The demonstration also showed that short-term carbon futures data could assist speculators to conduct a reliable short-term out of sample forecast of carbon futures prices within the closer period. The paper offers practical assistance to carbon futures speculators and is equally important for academic studies for business and economic students on discussions and research bordering on carbon emissions, carbon trading, environmental economics and sustainable development. More carbon short-term forecasting is encouraged – such research should compare short-term forecasting of carbon futures amongst different carbon markets.

scholarly journals Deep-Learning Forecasting Method for Electric Power Load via Attention-Based Encoder-Decoder with Bayesian Optimization

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1596
Author(s):  
Xue-Bo Jin ◽  
Wei-Zhen Zheng ◽  
Jian-Lei Kong ◽  
Xiao-Yi Wang ◽  
Yu-Ting Bai ◽  
...  
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Time Series Data ◽  
Load Forecasting ◽  
Bayesian Optimization ◽  
Series Data ◽  
Short Term ◽  
Proposed Model ◽  
Power Load

Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.

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