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Forecasting Macedonian GDP: Evaluation of different models for short term forecasting

Forecasting Macedonian GDP: Evaluation of different models for short term forecasting

We evaluate the forecasting performance of six different models for short-term forecasting of Macedonian GDP: 1) ARIMA model; 2) AR model estimated by the Kalman filter; 3) model that explains Macedonian GDP as a function of the foreign demand; 4) small structural model that links GDP components to a small set of explanatory variables; 5) static factor model that links GDP to the current values of several principal components obtained from a set of high-frequency indicators; 6) FAVAR model that explains GDP through its own lags and lags of the principal components. The comparison is done on the grounds of the Root Mean Squared Error and the Mean Absolute Error of the one-quarter- ahead forecasts. Results indicate that the static factor model outperforms the other models, providing evidence that information from large dataset can indeed improve the forecasts and suggesting that future efforts should be directed towards developing a state-of-the-art dynamic factor model. The simple model that links domestic GDP to foreign demand comes second, showing that simplicity must not be dismissed. The small structural model that explains every GDP component as a function of economic determinants comes third, “reviving” the interest in these old-school models, at least for the case of Macedonia.
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Short Term Forecasting Analysis for Municipal Water Demand

Short Term Forecasting Analysis for Municipal Water Demand

The objective of this study is to analyze short-term water consumption dynamics in El Paso, Texas. A growing metropolitan economy located in the desert Southwest region of the United States, El Paso has historically faced pronounced water supply constraints. Recurring droughts place additional pressure on local water supplies (Washington-Valdez, 2013). In response to projected shortages, El Paso Water Utilities (EPWU) adopted a comprehensive conservation strategy in 1991 (Tennyson & Parker, 2007). The subsequent moderation in the annual growth of water consumption is shown in Figure 1. Water demand forecasts and simulations are useful for both designing and evaluating conservation policies such as those put in place by EPWU in recent decades (Little & Moreau, 1991; White et al., 2003). Also, an arid climate, a growing population, and scarce water resources combine to make reliable forecasts essential to ensuring an adequate supply of water during peak-demand seasons in El Paso.
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Long term and short term forecasting 
		techniques for regional airport planning

Long term and short term forecasting techniques for regional airport planning

The annual number of passengers traveling with commercial air transport has increased substantially in recent years and is expected to continue increasing, with regional airports experiencing extra strong growth. Both the number of flight movements and the average load factor of each flight are increasing. In the Airbus forecast of 2015-2034, the global number of revenue passenger kilometers (RPK) is expected to double between 2014 and 2034, while the intra-Central European market is forecast to experience 4.4% annual growth (Airbus, 2015). The growth in demand for air traffic is partially driven by macroeconomic factors such as increased globalization and the change of travel behavior arising from demographic changes during economic upswings, particularly in Asian and eastern European economies. Another factor is the introduction in the 1990s of Low Cost Carriers (LCC) such as Ryanair and Easyjet, which has stimulated demand by introducing low fare flights. The price pressure has proved challenging to the established airlines, often referred to as Former Flag Carriers (FFC) or Legacy Carriers, and has led to an industry-wide lowering of fares. As airlines search to reduce costs, regional airports have experienced an increase in attractive power; since smaller and less used airports don't experience the congestion found at bigger airports, operating at these often increases productivity for the airlines. For example, in the Frankfurt-London route, Ryanair flying between Stansted-Hahn has 33% better productivity of aircraft and crew than Lufthansa has flying between the bigger airports Heathrow-Frankfurt. This is due to the less time spent being idle in queues, both on ground and in the air (Dennis, 2008). From the perspective of the management of a regional airport, the fast growth in number of passengers puts pressure on an effective planning of the capacity of the airport. Capacity improvements in airport infrastructure represents large and lumpy capital investments and long-term forecasts of passenger volumes and peak hour volumes are therefore of
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Short term forecasting of the US unemployment rate

Short term forecasting of the US unemployment rate

finds eight factors in the FRED-MD database. However, using this number of factors would require many more observations in the forecasting models, so that the first forecast could only be made for a later date. Because one of the aims of this paper is to investigate if high-frequency data has better forecasting performance than the usual benchmark models, especially during the Great Recession in 2008/09, the first forecast should be made for 2008 and not later. Hence, the forecasting models have to be parsimonious concerning the number of predictors and the required observations. Consequently, the number of factors is reduced to four. In addition, using too many factors might lead to overfitting and this would result in a poor forecasting performance. Figure 4 in Appendix A.3 shows a Scree plot of the estimated factors and Table 5 in Appendix A.3 illustrates that the first four factors explain 34 % of the variation of the FRED-MD data, while eight factors explain 47,5 % of the variation.
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Smart Water: Short-Term Forecasting Application in Water Utilities

Smart Water: Short-Term Forecasting Application in Water Utilities

had a drastic error decrease when fed with less historical data. The reason is due to the data itself instead of the model structure. A major change in water consumption profiles had occurred in the region where the utility is located. Greenhouses, the main consumer, started to switch from growing vegetables to Marijuana after the new Canadian legalization act occurred in 2018. The change in agricultural activities in the studied area had affected water demand profiles and left the historical data with little information to add. Another important observation is the dramatic error decrease in NARX model compared to NAR model. (3) Adding exogenous parameters to the nonlinear model has improved overall forecasting performance. NARX average overall performance in terms of error, MAPE, decreased by 30% and 25% compared to NAR models forecasting 24 hours ahead and 1 week ahead, respectively. NARX average error also decreased, as mentioned before, by 30% and 36% compared to SARIMA models. Even though NAR and NARX models have a similar structure, the inclusion of exogenous parameters has advantaged the ANN model. Again, this is due to extra correlations drawn from the extra inputs.
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Short term forecasting   review of recent experience

Short term forecasting review of recent experience

By the December 1973 issue this had been revised to 15*75 per cent.The rise, in both value and volume, o f retail sales in' the first few months o f 1973 was seen as temporary, given tha[r]

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AI based Short Term Electric Time Series Forecasting

AI based Short Term Electric Time Series Forecasting

Daily electricity data has been collected for India from 01 April, 2012 to 31 July, 2018 of hydro generation (in million units) of a power plant, Energy Met (in million units) and peak demand (in mega watt) [19] to develop short-term forecasting model. In this manuscript, simulation results of conventional models, i.e., AR, MA, ARMA and ARIMA with artificial neural network model for the electric forecasting are compared. Additionally, the best selection of hidden layer neurons with minimum forecasting error is also discussed. The first 2297 days is employed for training and validation purpose; however, remaining fourteen days is saved for testing purpose. The variations of various data sets, i.e., daily hydro generation, daily energy met, and daily peak demand are depicted in Figs. 1-3.
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INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

INDOOR GLOBAL PATH PLANNING BASED ON CRITICAL CELLS USING DIJKSTRA ALGORITHM

Forecasting is the process of making proclamations about events whose actual outcomes have not yet been observed or can define it as estimates of future value [1]. Load forecasting is an estimation of power demand at some future period. Load forecasting used by Power Utilities Company to predict the amount of power needed to supply the demand. Electric load forecasting has been a major area of research since the last millennium and it is a key to realization for many of the decision makers in the energy division, from power generation to operation of the system [2]. Electric industry wants to predict load demands in the short, medium and long term. Load forecasting can be classified into 3 different types according to the forecast period short-term forecasting, medium-term forecasting and long-term forecasting. Short-term forecasting usually makes forecasts from one hour to one week, medium-term forecasting concerns the future electric load from a week to a month, and long-term forecasting often predicts the load of one year or even longer. Due to research interest and industrial necessities short term load forecasting has gained great attention compared to others. The short-term forecasting is used for guiding and organization
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Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks

Short-term and Medium-term Gas Demand Load Forecasting by Neural Networks

Almost all short-term forecasting techniques use as independent variables certain weather condition information such as temperature, humidity or wind speed. After many processes of trial and error for daily gas consumption prediction, since some variables like day type (i.e. working day and holiday), wet bulb temperature and gas price haven’t any effect on the network performance thus they are deleted from the model input for the simplicity of the network and 29 desired network inputs are considered. At the input, meteorological parameters (i.e. daily effective temperature, cloudiness, rain rate and wind velocity) and also the gas consumption for the previous five days are fixed. The meteorological parameters for the prediction day are also considered as the network input. However, at the output, the gas consumption rate for the prediction date is estimated [23,24].
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Fuzzy Logic based Short-Term Load Forecasting

Fuzzy Logic based Short-Term Load Forecasting

The estimation of future active loads at various load buses is known as load forecasting. The capacities of the transmission, generation, and distribution strictly depend on accurate load forecasting of the system. The Energy Management System (EMS) demands precise forecasting and Short Term Load Forecasting gives improved and accurate Results [3]. Accurate forecasts of the system load in advance can help the system operator to accomplish a variety of tasks like scheduling of fuel purchases, economic scheduling of generating capacity and system security assessment. Load forecasting techniques are of three types such as short-term forecasting, mid- range forecasting, and long-term forecasting. All these types of forecasting methods are valuable for different types of systems.
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FORECASTING ENERGY CONSUMPTION IN SHORT-TERM AND LONG-TERM PERIOD BY USING ARIMAX MODEL IN THE CONSTRUCTION AND MATERIALS SECTOR IN THAILAND

FORECASTING ENERGY CONSUMPTION IN SHORT-TERM AND LONG-TERM PERIOD BY USING ARIMAX MODEL IN THE CONSTRUCTION AND MATERIALS SECTOR IN THAILAND

average rising to 37.32% in 2036 and the model 3 (2017–2046) energy consumption volume in- creased steadily as well and average rising to 49.72% in 2046. However, that model 1, model 2, and model 3 were tested the effectiveness of the model compared with actual value found that both models are highly effective with the low deviation can be used to decision making that shown in MAPE equal to 1.01, 1.11, and 1.78, re- spectively, (less than 3%) and test results showed that correlogram, the modeling value, can be used as the best model for predicting and forecasting the lowest tolerances value.
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Short Term Load Forecasting Using Soft Computing Techniques

Short Term Load Forecasting Using Soft Computing Techniques

Short-term load forecasting (STLF) is an essential tech- nique in power system planning, operation and control, load management and unit commitment. Accurate load forecasting will lead to appropriate scheduling and plan- ning with much lower costs on the operation of power systems [1 – 6]. Traditional load forecasting methods, such as regression model [7] gray forecasting model [8,9] and time series [10,11] do not consider the influence of all kind of random disturbances into account. At recent years artificial intelligence are introduced for load fore- casting [12 – 17]. Various types of artificial neural net- work and fuzzy logic have been proposed for short term load forecasting. They enhanced the forecasting accuracy compared with the conventional time series method. The ANN has the ability of self learning and non-linear ap- proximations, but it lacks the inference common in hu- man beings and therefore requires massive amount of training data, which is an intensive time consuming proc- ess. On the other hand fuzzy logic can solve uncertainty, but traditional fuzzy system is largely dependent on the knowledge and experiences of experts and operators, and is difficult to obtain a satisfied forecasting result espe- cially when the information is incomplete or insufficient.
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A Literature Survey of Load Forecasting Methods and Impact of Different Factors on Load Forecasting

A Literature Survey of Load Forecasting Methods and Impact of Different Factors on Load Forecasting

Time factor: Time factor in case of STLF is most concerned thing for precise load forecasting because SLTF is done on hourly basis. A load demand curve is published in a report by Sri Lanka Government [16] as shown in Figure (1).Curve showing the peak demand at 18HRS. So a uniform analysis for load forecasting is not enough. Close monitoring of load in hourly basis will give good forecast. Also load at same time in summer and winter varies with a large margin. Certain changes in the load pattern occur gradually in response to seasonal variations such as the number of daylight hours and the changes in temperature. Figure (2) shows the system peak occurs with a steep increase from 18:00 to 19:00 and depreciates with a slow rate of decreasing which takes about 3 hours. (From 19:00 to 22:00) This feature is common for all three curves. General opinion on the night peak is that it is predominately governed by domestic activities and lighting. Morning peak of weekdays (recorded at 06:00) is rather symmetrical, which consists with rapid increase and a rapid decrease. However, when considering only Sundays, the curve does not show a significant peak demand, but just a slight increase, which is recorded at 06:30.
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HYBRID AND INTEGRATED APPROACH TO SHORT TERM LOAD FORECASTING

HYBRID AND INTEGRATED APPROACH TO SHORT TERM LOAD FORECASTING

Two Layer Neural Network Power Systems architecture was chosen for analysis. Backpropagation algorithm was implemented with and without training. Tan-sigmoid function as in has been chosen in the hidden layer and purelin (linear) transfer function in the output layer. This is a useful structure for function approximation problem. Forecasting was tested to the neural networks based on designed network architecture described in Table-IV.

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Short Term Economic Forecasting and its Application in Ireland

Short Term Economic Forecasting and its Application in Ireland

National Product and Expenditure12 The sum total of the preceding discussion clearly indicates a further improvement in the performance of the Irish economy¯ It appears that I96r, too, X[r]

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The influence of differential privacy on short term electric load forecasting

The influence of differential privacy on short term electric load forecasting

The closest related to our work are differentially private smart metering concepts. Ács and Castelluccia were the first to apply Differential Privacy on smart metering data. In their work (Acs and Castelluccia 2011), a distributed Laplace mechanism is applied using Gamma distributions before the data is mixed with other smart meters in an aggre- gation group. Bao and Lu (2015) investigated further the security and fault tolerance properties of the aggregation and mixing protocol. Eibl and Engel (2017) introduced post-processing to be applied on the perturbed data to improve the utility while still guaranteeing the same privacy level. They also discuss the required number of house- holds in an aggregation group in order to be useful to the data analyst. Böhler et al. (2017) suggest using Differential Privacy with relaxed sensitivity and a privacy-preserving correction algorithm in IoT scenarios to still allow outlier detection while protecting the majority of households. Barbosa et al. (2016) also discussed filtering techniques to improve utility after the noise has been added to the aggregate. Their work evaluates the protection of individual appliances in single households by considering multiple device sensitivities in load profiles and by using Differential Identifiability. However, they do not address the compatibility condition m = 2 to allow utilizing Differential Identifiabil- ity in Differential Privacy scenarios. Besides Differential Identifiability, another method for rationally choosing was proposed in (Hsu et al. 2014). Yet, this approach is purely economically driven and introduces a handful of new parameters depending again on subjective assumptions on a given scenario. In contrast, focusing more on unconditional privacy, we further analyze Differential Identifiability. From Ács et al. (2011) we bor- rowed the way how to generate Laplacian noise in a distributed way. While in (Acs and Castelluccia 2011) the focus is on the aggregation protocol, we further improve composition and connect to Differential Identifiability and load forecasting with utility guarantees.
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A Short Term Electricity Price Forecasting Scheme for Power Market

A Short Term Electricity Price Forecasting Scheme for Power Market

Future work will continue to refine the optimal models for predicting electricity price and build a rolling forecasting process in electricity market to achieve more accurate results step-by-step. Alternatively, other statistical models, e.g., an artificial neural net- work (ANN) model or an AR model combined with trend modelling [16] will be estab- lished and their forecast accuracies will be compared with the ARIMA models. The op- timum models will be employed to generate the electricity price forecasts of high relia- bility and accuracy.

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Short-Term Load Forecasting Using Artificial Neural Network

Short-Term Load Forecasting Using Artificial Neural Network

Electric load demand is a function of weather variables and human social activities, industrial activities as well as community developmental level to mention a few [2-7]. Statistical techniques and Expert system techniques have failed to adequately address this issue [2-10]. The daily operation and planning activities of an electric utility requires the prediction of electricity demand of its customers. In general, the required load forecasts can be categorized into short-term, mid-term, and long-term forecasts. The short-term forecasts refer to hourly prediction of the load for a lead time ranging from one hour to several days out. The mid-term forecasts can either be hourly or peak load forecasts for a forecast horizon of one to several months ahead. Scheduling of fuel purchases, load flow studies or contingency analysis, and planning for energy, while the long-term forecasts refer to forecasts made for one to several years in the future. The quality of short-term hourly load forecasts has a significant impact on the economic operation of the electric utility since decisions such as economic scheduling of generating capacity, transactions such as ATC (Available Transmission Capacity) are based on these forecasts and they have significant economic consequences.
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Forecasting Of Short Term Wind Power Using ARIMA Method

Forecasting Of Short Term Wind Power Using ARIMA Method

Abstract- Wind power, i.e., electrical energy produced making use of the wind resource, is being nowadays constantly connected to the electrical system. This has a non-negligible impact, raising issues like network stability and security of the supply. An accurate forecast of the available wind energy for the forthcoming hours is crucial, so that proper planning and scheduling of the conventional generation units can be performed. Also, with the liberalization of the electrical markets worldwide, the wind power forecasting reveals itself critical to assure that the bids are placed with a minimum possible risk. The main application for wind power forecasting is to reduce the need for balancing energy and reserve power, which are needed to integrate wind power within the balancing of supply and demand in the electricity supply system. At times of maintenance it is required to know how much power would have been generated and should be supplied by other source. This work addresses the issue of forecasting wind power with statistical model, the Autoregressive Integrated Moving Average (ARIMA). The basic theory and the respective application of these models to perform wind power prediction are presented in this paper. Furthermore, their forecasting abilities are shown with the help of graphs.
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A Critical Review on Employed Techniques for Short Term Load Forecasting

A Critical Review on Employed Techniques for Short Term Load Forecasting

is estimated. Load forecasting is a tricky task because first, the load series is complex and exhibits several levels of seasonality and second, the load at a given hour is dependent not only on the load at the previous day, but also at the same hour on the load on the previous day and previous week and because there are many important exogenous variables that must be considered [2]. Depending on the time zone of the planning strategies [3, 4] the load forecasting can be divided into four categories namely very short term load forecasting, short term load forecasting, midterm load forecasting and long term load forecasting. Short-term load forecasting technique that considers electricity price as one of the main characteristics of the system load, demonstrating the importance of considering pricing when predicting loading in today’s electricity markets [5]. However, it is impossible to predict the next year peak load with the similar accuracy since accurate long-term weather forecasts are not available. For the next year peak forecast, it is possible to provide the probability distribution of the load based on historical weather observations [6]. Advanced load forecasting tools or applications gives appropriate future long term load requirements [7]. Load forecasting has been an integral part in the efficient planning, operation and maintenance of a power system. Short term load forecasting is necessary for the control and scheduling operations of a power system [8].
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