Many data-driven electricity **price** **forecasting** approaches are mainly focused on short- **term** electricity **price** **forecasting**. This generally includes timescales from one hour- ahead to one day ahead. There has been less work on the medium **term** (weeks to years) and **long** **term** (many years) horizon. Additionally, since deregulation of electric- ity markets has began fairly recently around the world, limited explanatory data exist for medium and **long** **term** **price** **forecasting**. It is noteworthy that many of the **forecasting** engines such as artificial neural networks need a large data set for training, and thus are less applicable to medium and **long** **term** **forecasting** because of its availability [26]. Developing medium and **long** **term** **forecasting** is essential to many market activities, in- cluding generation expansion planning, maintenance scheduling, bilateral contracting, fuel contracting, and developing investment and hedging strategies. In particular, in- creased volatility in **price** means that the **power** industry has become more interested in risk management methods in all time horizons [25]. The situation becomes more com- plicated when comparing forecasts between several **power** markets. The dependability of cross hedging, or using futures contracts from di fferent markets, as a risk-reducing instrument depends highly on the inter-market spot and future **price** **correlation** [5].

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energy efficiency and reducing wastage [7, 8]. On the one hand, RTP tariff is benefit to **power** grid as it offers specific **price** instructions for participants to average the **power** usage at different time so that alleviates the load burden of **power** grid especially in peak demand time. On the other hand, such an electricity tariff encourages consumption by **price** reduction during periods of abundance and allows customers to have multiple choices to determine the time of electricity consumption. The participants in electricity market can regulate the operating time of electrical devices automatically or manually during high-**price** periods and gain the benefits from low-**price** periods via DRM, thus achieving the aims of reducing energy usage and saving electric bills for themselves [5, 9–11]. Therefore, the research on RTP tariff is of interest to researchers, **production** companies, investors, independent market operators and large industrial consumers in recent years [12, 13].

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Page | 26 Equations 2.8, 2.9 and 2.10 are examples of functions that are nonlinear in both the variables and the parameters. Therefore, the linearity assumption of linear regression is not met and we should not use the R² statistic to assess the goodness of fit. Polynomial functions shown in Equation 2.5 are linear in the parameters. Therefore, we can use linear regression as **long** the assumptions for linear regression are met. If this is not the case, we should be cautious with the interpretation of the R² statistic. When using the method of nonlinear least squares, the way in which the unknown parameters in the function are estimated is conceptually the same as it is in linear least squares regression. Parameters are calculated so the total SSE is minimized. However, the major **cost** of moving to nonlinear least squares regression is the need to use iterative optimization procedures to compute the parameter estimates (Bates & Watts, 2008). With functions that are linear in the parameters, the least squares estimates of the parameters can always be obtained analytically, this is not the case for nonlinear models. The use of iterative procedures requires the user to provide starting values for the unknown parameters before the software can begin the optimization. The starting values must be reasonably close to the as yet unknown parameter estimates or the optimization procedure may not converge. Bad starting values can also cause software to converge to a local minimum rather than the global minimum (Bates & Watts, 2008). In Subsection 2.5.3, we discussed some optimization algorithms that were used by Lydia et al. (2013).

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The artificial neural networks **model** outperforms the multiple linear regression analysis MLR **model** and the persistence **model**. The performance of the ANN depends on how well it is trained and on the quality of the data that is used. The feed-forward ANN with 3 weather variables and with step size for forecasts performed better than the other The residuals plot of ANN **model** and the **correlation** coefficient plots for solar **power** forecasts of ANN, MLR and Persistence models recursive neural networks. The normalized input data doesn’t improve the performance, but removing the night hours slightly improves the **model** performance. Plotting the data, investigating the **correlation** and sensitivity analysis between the variables, as well as data cleansing of outliers are essential data preparation steps before building the **forecasting** **model**. In the clear sky hours, the **model** produces more accurate forecasts than cloudy hours. The more accurate weather forecasts we use, the more accurate solar **power** forecasts will be produced. Using the classification variables and the interactions between the variables enhances the performance of the MLR **model** significantly but this is not the case for the ANN **model**. With additional historical data, the **model** performance will improve.

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That is why, dimensionality reduction techniques had been widely used in economics, financial or demographical applications, among others. However, the use of this methodology was not extended in the energy context till relatively recently. But some recent publications have demonstrated that these techniques are very powerful ones for **long**-**term** **forecasting** of electricity prices. Here, we adapt this methodology to the case of modeling and **forecasting** aggregated hourly wind **power** **production** in two close regions, where apart from the relationship among hourly series we have to consider the relationship due to closeness and consequent sharing of meteorological conditions.

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Abstract: It is exceedingly difficult to get accurate predictions for single traditional prediction models because of the volatility, non-linear increment and complexity of **power** loads. In order to improve the accuracy for **long**-**term** **power** load **forecasting**, the multivariate exponential weighting grey prediction **model**, residual grey prediction **model**, dynamic and equal-dimensional information grey prediction **model** and equal time sequence grey prediction **model** were constructed and weighted by **correlation** so that an improved combination grey prediction **model** could be built to make a prediction and for empirical analysis. The example shows that the volatility can be effectively reduced by the multivariate exponential weighting **model** and dynamic equal-dimensional information **model**. Similarly, the residual **model** and equal time sequence **model** are suitable for the **power** load **forecasting** with non-linear increasing trend. Considering all kinds of features of **power** loads, the constructed combination **model** can improve the accuracy of **power** load **forecasting** effectively and ensure the economic and safe operation for **power** system.

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Stock Market still depends on human intuition and emotions. More robust and accurate prediction includes new feed analysis from wide range of online media platforms like Reddit, Facebook and Twitter where sentiments can be extracted. In 2013, a scientific research paper published by Helen Susannah Moat and Tobias Preis in which they exhibited a **correlation** between changes in the number of views of English Wikipedia articles relating to financial topics and subsequent large stock market moves [7][8]. The use of text mining algorithms combine with deep learning techniques has receiver prodigious attention since last decade. By using Text sentiment analysis fused with RNNs and LSTMs to get more accurate predictions and decrease error.

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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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Abstract-- Accurate **forecasting** tools are essential in the operation of electric **power** systems, especially in regulated electricity market. Electricity **price** **forecasting** is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-**term** hourly electricity **price** **forecasting**. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for **forecasting** electricity **price** and **price** spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by **correlation** significance of the historical data. Factors impacting the electricity **price** **forecasting**, including historical **price** factors, load factors and wind **production** factors are discussed. A **price** spike index (CWI) is defined for spike detection and **forecasting**. Using proposed approach we created several **forecasting** models of diverse **model** complexity. The method is validated using the European Energy Exchange (EEX) electricity **price** data records. Finally, results are discussed with respect to **price** volatility, with emphasis on the **price** **forecasting** accuracy.

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In the last decade, widespread research has been published on load **forecasting** due to its potential to be utilized in smart buildings, grids and cities [3]. The importance of load forecast is further increased as efficiency of **power** system is affected due to an under and overestimation of load demand. The underestimation of the electrical load demand may show a very negative effect on the demand response, moreover it is also difficult to manage overload conditions as a large backup storage almost impractical for economic reason. In case of overestimation, it may increase the **power** surplus and **production** **cost** leading to very inefficient system operation [4]. Load demand forecast also play a vital role in optimum unit commitment, control of spinning reserve, evaluation of sales and purchase contracts between various energy companies.

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Abstract: The oil market has its effect straightforwardly or in a roundabout way on the income distribution of countries influencing the stock market, average **cost** for basic items, education, essential commodities and many more. Moreover, in response, oil costs are influenced by various elements. In this manner, there is an unmistakable need to figure the oil value patterns. This challenge has been undertaken by numerous studies. Machine Learning has been the crucial crux for a lot of them. LSTM models have been used time and again for time series **forecasting**. This article studies the LSTM neural network and its use to predict future trends of Brent oil prices based on the previous **price** of Brent oil.

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Worldwide electricity market reform makes the **power** industry transform from gradu- ally monopoly to competition. As market participants, each supplier and consumer wants to get the most benefits [1]. If the electricity **price** can be predicted accurately, generation side could handle the market dynamic and make optimal **power** generation plan. Demand side could select their time of **power** use and choose the electric quantity they want to buy for reducing the **cost** and increasing market competitiveness [2]. For regulators, grid reference **price** forecast results can help to improve the monitoring ca- pability of electricity market operation and discover and resolve the problems in the Gao, G., Lo, K., Lu,

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Abstract. Electricity demand **forecasting** is an important part in energy management especially in electricity planning. Indonesia is a large country with a pattern of electricity consumption which continues to increase, therefor need to **forecasting** electricity demand in order to avoid unbalance demand and supply or deficit energy. LEAP (**Long**-range Energy Alternative Planning System) as a tool energy **model** and Indonesia as a case study. Basically, electricity demand is influenced by population, economy and electricity intensity. The purpose of this study is to provide understanding and application of electricity demand **forecasting** by using LEAP. The base year is 2010 and end year projection is 2025. The scenarios of simulated **model** consist of two scenarios. They are Business as Usual (BAU) and Government policy scenario. Results of both scenarios indicate that end year electricity demand **forecasting** in Indonesia increased more than two fold compared to base year.

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different datasets. We can see that TLSTM notably outper- forms all baselines on all datasets in this setting. In particu- lar, TLSTM is more robust to **long**-**term** error propagation. We observe two salient benefits of using TT-RNNs over the unfactorized models. First, MRNN and MLSTM can suffer from overfitting as the number of weights increases. Second, on traffic, unfactorized models also show consider- able instability in their **long**-**term** predictions. These results suggest that tensor-train neural networks learn more stable representations that generalize better for **long**-**term** horizons. Visualization of Predictions To get intuition for the learned models, we visualize the best performing TLSTM and baselines in Figure 6 for the Genz function “corner- peak” and the state-transition function. We can see that TLSTM can almost perfectly recover the original function,

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From the study with the use of ARIMAX **Model**, it has found that **model** 1 with the fore- casting period of 10 years, 2017–2026, gives the rate of energy consumption increased by 18.09%, while **model** 2, forecasted in the year of 2017 un- til 2036, indicates an increase in the energy con- sumption rate of 37.32%, and **model** 3, predicted within the period of 2017 until 2046 deemed to increase 49.72%. The outcomes from this study can be seemingly incorporated into both short- **term** and **long**-**term** national policies planning. Plus, the researcher has verified the accuracy of the actual data (Actual Data) and the quality of MAPE, and RMFE models. Moreover, any vul- nerable element towards spurious, such as Au- tocorrelation, Heteroskedasticity, and Multocol- linearity, has also been eliminated. To Thailand, it is necessary to apply the study’s **model**, that has been developed to achieve the maximum benefit. It should also be used for planning and decision-making to determine the country’s poli- cies in both short-**term** and **long**-**term** period. To achieve a sustainable development of the coun- try, Thailand must ensure and secure these three elements: a growing economy, a better environ- ment, and a better-living society. If any of these

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Similarly, an assessment of the global technical and economic hydropower potential at the global scale is presented by Gernaat et al. ( 2017 ). This study used high-resolution hy- drographic discharge (15”× 15”; 450 m at the Equator) and elevation (3”× 3”; 90 m at the Equator) maps to calculate **cost**-optimal dimensions and associated **production** poten- tial of two types of hydropower systems: river **power** plants and diversion canal **power** plants (following the definitions of Wagner and Mathur , 2011 ). The authors considered that globally, hydropower projects would: i) follow **cost** equations from Norwegian and US hydropower tender and contracts (which might underestimate the costs in develop- ing countries), and ii) deploy fully in all rivers across the globe, excluding only the first 200 km upstream of basin outlets of rivers deeper than 4 m (river mouth restriction) and the area in the vicinity of large bodies of water such as lakes or wide rivers. These assumptions overlook policy and other social factors that might impact the development of hydroelectricity. Gernaat et al. also assessed the effect of climate change. Globally, a slight increase is seen (2 – 10%) that consistently occurs in Africa (4 – 18%) and Asia Pacific (3 – 6%), while Europe shows a consistent decrease (-2 – -3%). North and South America are less consistent over across the climate models.

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quasi-periodic patterns. Moreover, (seasonal) rainfall, famines, sleeping patterns, or traffic congestion pos- sess periodic trends. To make informed decisions, it is often necessary to forecast the system’s evolution a **long** time ahead. With good models that account for the inherent periodicity of the data we can make well- informed **long**-**term** predictions and, thus, decisions. In the context of regression, non-parametric Gaussian processes (GPs) (O’Hagan, 1978; Neal, 1997; Williams and Rasmussen, 1996) are the state-of-the-art method since they allow for flexible modeling while express- ing uncertainties in a consistent way. Assumptions re- garding the system’s characteristics, e.g., smoothness or periodicity, can be encoded explicitly into the kernel of the GP (Rasmussen and Williams, 2006). Periodic GPs are used by Durrande et al. (2013) to detect pe- riodically expressed genes or by Reece and Roberts (2010) in the context of target tracking.

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In geostatistical modelling, the spatial Markov chain, or the Markov chain geostatistics was developed based on the multi- dimensional Markov chain random field theory [24]. The spatial Markov chain moves or jumps in a space and decides its state at any unobserved location through interactions with its nearest known neighbours in different directions. Inspired by this idea, a first-order discrete spatio-temporal Markov chain (STMC) **model** is developed for very-short-**term** WPF. In this **model**, the spatio- temporal transitions between the wind **power** output states of reference wind farms and a target wind farm are additionally considered as an extension of traditional Markov chain that only considers temporal states transitions. The forecasts derived from spatio-temporal Markov regarding multiple reference wind farms are optimally combined using sparse modelling to obtain the final forecasts for target wind farm. A case study is carried out to demonstrate the effectiveness of the proposed method.

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In the second stage, the forecast distributions were obtained from the estimated **model** using a mixture of temperature and residual simulations, and future assumed demographical and economic scenarios. A new seasonal bootstrapping method with variable blocks is applied to resample residuals and temperatures. The temperature bootstrap is designed to capture the serial **correlation** that is present in the data due to weather systems moving across South Aus- tralia. More than 2000 years of temperatures are simulated to estimate the forecast density. Finally, to evaluate the **forecasting** performance of the **model**, we compare the actual demand of a summer with two different types of predictions: ex ante forecasts and ex post forecasts. Specifically, ex ante forecasts are the forecasts made in advance using whatever information is available at the time. On the other hand, ex post forecasts are those that are made using information on the “driver variables” that is only known after the event being forecast. The difference between the ex ante forecasts and ex post forecasts will provide a measure of the effectiveness of the **model** for **forecasting** (taking out the effect of the forecast errors in the input variables). The results show good **forecasting** capacity of the proposed **model** at predicting the distribution of **long**-**term** electricity demand.

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The presentation may contain “forward looking statements” within the meaning of the United States private securities litigation reform act of 1995 and “forward looking information” with [r]

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