A comparison of the short term forecasting accuracy of econometric and naive extrapolation models of market share

1987 ◽  
Vol 3 (3-4) ◽  
pp. 423-437 ◽  
Author(s):  
Roderick J. Brodie ◽  
Cornelis A. De Kluyver
Keyword(s):  
Market Share ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

scholarly journals Short-Term Forecasting of Electricity Supply and Demand by Using the Wavelet-PSO-NNs-SO Technique for Searching in Big Data of Iran’s Electricity Market

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 43 ◽  
Author(s):  
Mesbaholdin Salami ◽  
Farzad Movahedi Sobhani ◽  
Mohammad Ghazizadeh
Keyword(s):  
Neural Networks ◽  
Electricity Market ◽  
Simulation Optimization ◽  
Supply And Demand ◽  
Electricity Supply ◽  
Wavelet Neural Networks ◽  
Short Term ◽  
Swarm Optimization ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

The databases of Iran’s electricity market have been storing large sizes of data. Retail buyers and retailers will operate in Iran’s electricity market in the foreseeable future when smart grids are implemented thoroughly across Iran. As a result, there will be very much larger data of the electricity market in the future than ever before. If certain methods are devised to perform quick search in such large sizes of stored data, it will be possible to improve the forecasting accuracy of important variables in Iran’s electricity market. In this paper, available methods were employed to develop a new technique of Wavelet-Neural Networks-Particle Swarm Optimization-Simulation-Optimization (WT-NNPSO-SO) with the purpose of searching in Big Data stored in the electricity market and improving the accuracy of short-term forecasting of electricity supply and demand. The electricity market data exploration approach was based on the simulation-optimization algorithms. It was combined with the Wavelet-Neural Networks-Particle Swarm Optimization (Wavelet-NNPSO) method to improve the forecasting accuracy with the assumption Length of Training Data (LOTD) increased. In comparison with previous techniques, the runtime of the proposed technique was improved in larger sizes of data due to the use of metaheuristic algorithms. The findings were dealt with in the Results section.

scholarly journals Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

2020 ◽  
Author(s):  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Nalika Ulapane
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Time Series Forecasting ◽  
Sensor Data ◽  
Alternative Measure ◽  
Sewer Pipe ◽  
Short Term ◽  
Pipe Surface ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

scholarly journals Short-term Time Series Forecasting of Concrete Sewer Pipe Surface Temperature

2020 ◽  
Author(s):  
Karthick Thiyagarajan ◽  
sarath kodagoda ◽  
Nalika Ulapane
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Time Series Forecasting ◽  
Sensor Data ◽  
Alternative Measure ◽  
Sewer Pipe ◽  
Short Term ◽  
Pipe Surface ◽  
Forecasting Accuracy ◽  
Short Term Forecasting

Microbial corrosion is considered the main reason for multi-billion dollar sewer asset degradation. Sewer pipe surface temperature is a vital parameter for predicting the micro-biologically induced concrete corrosion. Due to this important measure, a surface temperature sensor suite was recently developed and tested in an aggressive sewer environment. The sensors can fail and they may also put offline during the period of scheduled maintenance. In such situations, time series forecasting of sensor data can be an alternative measure for the operators managing the sewer network. In this regard, this paper focuses on the short-term forecasting of sensor measurements. The evaluation was carried out by forecasting the sensor measurements for different time periods and evaluated with different forecasting models. The ETS model leads to high short-term forecasting accuracy and the ARIMA model leads to high long-term forecasting accuracy. The models were evaluated on real data captured in a Sydney sewer

scholarly journals Short-Term Forecasting of Loads and Wind Power for Latvian Power System: Accuracy and Capacity of the Developed Tools

2016 ◽  
Vol 53 (2) ◽  
pp. 3-13 ◽  
Author(s):  
V. Radziukynas ◽  
A. Klementavičius
Keyword(s):  
Power System ◽  
Wind Power ◽  
Arima Models ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Additional Input ◽  
Input Variables ◽  
Short Term Forecasting ◽  
Performance Results ◽  
Forecasting Errors

Abstract The paper analyses the performance results of the recently developed short-term forecasting suit for the Latvian power system. The system load and wind power are forecasted using ANN and ARIMA models, respectively, and the forecasting accuracy is evaluated in terms of errors, mean absolute errors and mean absolute percentage errors. The investigation of influence of additional input variables on load forecasting errors is performed. The interplay of hourly loads and wind power forecasting errors is also evaluated for the Latvian power system with historical loads (the year 2011) and planned wind power capacities (the year 2023).

scholarly journals A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2777 ◽  
Author(s):  
Chiou-Jye Huang ◽  
Ping-Huan Kuo
Keyword(s):  
Wind Speed ◽  
Absolute Error ◽  
Accuracy Evaluation ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Evaluation Indexes ◽  
Wind Speed Forecasting ◽  
Forecasting Performance ◽  
Neural Network Algorithm ◽  
Short Term Forecasting

To efficiently manage unstable wind power generation, precise short-term wind speed forecasting is critical. To overcome the challenges in wind speed forecasting, this paper proposes a new convolutional neural network algorithm for short-term forecasting. In this paper, the forecasting performance of the proposed algorithm was compared to that of four other artificial intelligence algorithms commonly used in wind speed forecasting. Numerical testing results based on data from a designated wind site in Taiwan were used to demonstrate the efficiency of above-mentioned proposed learning method. Mean absolute error (MAE) and root-mean-square error (RMSE) were adopted as accuracy evaluation indexes in this paper. Experimental results indicate that the MAE and RMSE values of the proposed algorithm are 0.800227 and 0.999978, respectively, demonstrating very high forecasting accuracy.

scholarly journals Very Short-Term Photovoltaic Power Forecasting Using Stochastic Factors

2020 ◽  
Vol 13 (2) ◽  
pp. 188-195
Author(s):  
Kriangkamon Khumma ◽  
Kreangsak Tamee
Keyword(s):  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Minute Period ◽  
Filtering Technique ◽  
Stochastic Forecasting ◽  
Short Term Forecasting ◽  
Persistence Model ◽  
Power Forecasting

    This paper proposes a photovoltaic (PV) power forecasting model, using the application of a Gaussian blur algorithm filtering technique to estimate power output and the creation of a stochastic forecasting model. As a result, affected power can be forecasted from stochastic factors with machine learning and an artificial neural network. This model focuses on very short-term forecasting over a five minute period. As it uses only endogenous data, no exogenous data is needed.      To evaluate the model, results were compared to the persistence model, which has good short-term forecasting accuracy. This proposed PV forecasting model gained higher accuracy than the persistence model using stochastic factors.

scholarly journals The Short-Term Forecasting of Asymmetry Photovoltaic Power Based on the Feature Extraction of PV Power and SVM Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1777
Author(s):  
Lishu Wang ◽  
Yanhui Liu ◽  
Tianshu Li ◽  
Xinze Xie ◽  
Chengming Chang
Keyword(s):  
Power Output ◽  
Ensemble Empirical Mode Decomposition ◽  
Empirical Method ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Modified Particle Swarm Optimization ◽  
Mode Decomposition ◽  
Short Term Forecasting

To improve forecasting accuracy for photovoltaic (PV) power output, this paper proposes a hybrid method for forecasting the short-term PV power output. First, by introducing the noise level, an improved complementary ensemble empirical mode decomposition (EEMD) with adaptive noise (ICEEMDAN) is developed to determine the ensemble size and amplitude of the added white noise adaptively. ICEEMDAN can change PV power output with non-symmetry into intrinsic mode functions (IMFs) with symmetry. ICEEMDAN can enhance the forecasting accuracy for PV power by IMFs with physical meaning (not including spurious modes). Second, the selection method of relative modes (IF), which is determined by the comprehensive factor, including the shape factor, crest factor and Kurtosis, is introduced to adaptively classify the IMFs into groups including similar fluctuating components. The IF can avoid the drawbacks of threshold determination by an empirical method. Third, the modified particle swarm optimization (PSO) (MPSO) is proposed to optimize the hyper-parameters in the support vector machine (SVM) by introducing the piecewise inertial weight. MPSO can improve the global and local search ability to make the particles traverse the global space and strengthen the performance of local convergence. Finally, the proposed method (ICEEMDAN-IF-MPSO-SVM) is used to forecast the PV power output of each group individually, and then, the single forecasting result is reconstructed to obtain the desired forecasting result for PV power output. By comparison with the other typical methods, the proposed method is more suitable for forecasting PV power output.

scholarly journals The Impact of Data Filtration on the Accuracy of Multiple Time-Domain Forecasting for Photovoltaic Power Plants Generation

2020 ◽  
Vol 10 (22) ◽  
pp. 8265
Author(s):  
Stanislav A. Eroshenko ◽  
Alexandra I. Khalyasmaa ◽  
Denis A. Snegirev ◽  
Valeria V. Dubailova ◽  
Alexey M. Romanov ◽  
...  
Keyword(s):  
Power Generation ◽  
Time Domain ◽  
Power Plants ◽  
Multiple Time ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Photovoltaic Power ◽  
Operational Forecasting ◽  
Short Term Forecasting ◽  
Forecasting Errors

The paper reports the forecasting model for multiple time-domain photovoltaic power plants, developed in response to the necessity of bad weather days’ accurate and robust power generation forecasting. We provide a brief description of the piloted short-term forecasting system and place under close scrutiny the main sources of photovoltaic power plants’ generation forecasting errors. The effectiveness of the empirical approach versus unsupervised learning was investigated in application to source data filtration in order to improve the power generation forecasting accuracy for unstable weather conditions. The k-nearest neighbors’ methodology was justified to be optimal for initial data filtration, based on the clusterization results, associated with peculiar weather and seasonal conditions. The photovoltaic power plants’ forecasting accuracy improvement was further investigated for a one hour-ahead time-domain. It was proved that operational forecasting could be implemented based on the results of short-term day-ahead forecast mismatches predictions, which form the basis for multiple time-domain integrated forecasting tools. After a comparison of multiple time series forecasting approaches, operational forecasting was realized based on the second-order autoregression function and applied to short-term forecasting errors with the resulting accuracy of 87%. In the concluding part of the article the authors from the points of view of computational efficiency and scalability proposed the hardware system composition.

The causes of poverty in Russia

2020 ◽  
Vol 13 (1) ◽  
pp. 21-36
Author(s):  
I.S. Ivanchenko
Keyword(s):  
Regression Analysis ◽  
Regional Development ◽  
Financial Market ◽  
Russian Federation ◽  
Short Term ◽  
Financial Situation ◽  
Macroeconomic Variables ◽  
Development Plans ◽  
The Russian Federation ◽  
Short Term Forecasting

Subject. This article analyzes the changes in poverty of the population of the Russian Federation. Objectives. The article aims to identify macroeconomic variables that will have the most effective impact on reducing poverty in Russia. Methods. For the study, I used the methods of logical, comparative, and statistical analyses. Results. The article presents a list of macroeconomic variables that, according to Western scholars, can influence the incomes of the poorest stratum of society and the number of unemployed in the country. The regression analysis based on the selected variables reveals those ones that have a statistically significant impact on the financial situation of the Russian poor. Relevance. The results obtained can be used by the financial market mega-regulator to make anti-poverty decisions. In addition, the models built can be useful to the executive authorities at various levels for short-term forecasting of the number of unemployed and their income in drawing up regional development plans for the areas.

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