Electric power consumption - Forecasting - Statistical methods

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Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

Seasonal forecasting of hydrological drought in the Limpopo Basin: a comparison of statistical methods

The term “teleconnection” refers to the influence of some- times remote ocean regions on atmospheric variables, such as moisture content or precipitation. Past studies on southern African precipitation found predictability based on El Niño, the Indian Ocean and the Atlantic Ocean (Reason et al., 2006; Landman et al., 2005; Landman and Mason, 1999). However, the atmospheric circulation is very complex, sometimes hav- ing the effect that even strong El Niño events do not propa- gate to the region (Thomson et al., 2003). A reason might be that the ocean region south of Africa is the major source for precipitation in southern Africa (Gimeno et al., 2010). This region is characterised by the chaotic collision system of the warm Agulhas and the cold Antarctic circumpolar ocean cur- rent (see Fig. 1) (Peterson and Stramma, 1991). In the colli- sion process, warm Agulhas eddies can form, maintaining higher evaporation until they dissipate. There are more com- plex effects such as the Darwin sea level pressure (Manatsa et al., 2007), the linkage of the El Niño–Southern Oscillation (ENSO) with the Indian Ocean dipole (Yuan and Li, 2008) or the stratospheric quasi-biennial oscillation (Jury, 1996) and even the Antarctic ozone depletion (Manatsa et al., 2013). Despite that complexity, SST teleconnections remain the pre- ferred choice of predictors in seasonal forecasting (Landman et al., 2005; Landman and Mason, 1999; Funk et al., 2014). In this study, widely used climate indices are complemented with customised indices resulting from a composite and cor- relation analysis of SSTs in the Indian and Atlantic oceans.
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Multi-step short-term power consumption forecasting with a hybrid deep learning strategy

Multi-step short-term power consumption forecasting with a hybrid deep learning strategy

Abstract: Electric power consumption short-term forecasting for individual households is an important and challenging topic in the fields of AI-enhanced energy saving, smart grid planning, sustainable energy usage and electricity market bidding system design. Due to the variability of each household’s personalized activity, difficulties exist for traditional methods, such as auto-regressive moving average models, machine learning methods and non-deep neural networks, to provide accurate prediction for single household electric power consumption. Recent works show that the long short term memory (LSTM) neural network outperforms most of those traditional methods for power consumption forecasting problems. Nevertheless, two research gaps remain as unsolved problems in the literature. First, the prediction accuracy is still not reaching the practical level for real-world industrial applications. Second, most existing works only work on the one-step forecasting problem; the forecasting time is too short for practical usage. In this study, a hybrid deep learning neural network framework that combines convolutional neural network (CNN) with LSTM is proposed to further improve the prediction accuracy. The original short-term forecasting strategy is extended to a multi-step forecasting strategy to introduce more response time for electricity market bidding. Five real-world household power consumption datasets are studied, the proposed hybrid deep learning neural network outperforms most of the existing approaches, including auto-regressive integrated moving average (ARIMA) model, persistent model, support vector regression (SVR) and LSTM alone. In addition, we show a k-step power consumption forecasting strategy to promote the proposed framework for real-world application usage.
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Survey on Machine Learning based Electric Consumption Forecasting using Smart Meter Data

Survey on Machine Learning based Electric Consumption Forecasting using Smart Meter Data

In modern world the use of smart meter for controlling and managing electric power consumption is one of the technologies which helps both customer as well as electric power supplier. It is expected that 70% of the worlds' population over 6 billion people, will live in cities and surrounding regions by 2050, so cities need to be smart [1]. Reliable, efficient and seamless electric power and energy flow are the critical parts to energize and to power services like smart cities, buildings, factories, and transportations. To implement these important services without interruption the knowledge of smart meter and smart energy with the combination of electric power grid plays a great role [2]. Smart Energy has been a critical calculated worldview for future energy utilize, on account of restricted non-sustainable power source assets accessible on Earth and furthermore high cost of obtaining sustainable power sources. Using energy in more efficient and effective way and reducing the power supply-demand gap increases the performance of smart grid system. Effective electric power management and distribution without loss of energy are the most challenges of the current energy grid system. The concept of smart meter and smart grid aims fixing those challenges to providing quality services. Many data analytics researches has been done and need to be done, to provide smart grid with full capability to have fair distribution of electric energy and reduce electric
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A critical review of wind power forecasting methods - past, present and future

A critical review of wind power forecasting methods - past, present and future

This study critically reviewed investigations regarding wind power forecasting models, focusing on methods of analysis, prediction time scales, error measurements and accuracy improvements. It was concluded that under the same conditions, physical methods are more complex and need considerable computing resources, but suitable for medium to long-term prediction. On the other hand, statistical methods, which performed better in short to medium term periods, were easy to be modelled and inexpensive. A combination of these two major methods with their merits led to the promising hybrid methods. Besides wind speed, temperature, wind direction, relative humidity and air pressure were the most often used input features in reviewed studies. Additionally, the one-year period and the sampling rate of 10 min were the most common features used for input data. Based on the discussions in this paper, a flowchart for wind power prediction is put forward, allowing the users to select appropriate prediction procedure based on different time horizons, analysis methods, error measurements, etc.
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ANN and Statistical Theory Based Forecasting and Analysis of Power System Variables

ANN and Statistical Theory Based Forecasting and Analysis of Power System Variables

In this paper, load data for Kalwa region of Maharashtra State has been analyzed. The statistical methods such as Beta Probability Distribution Function & Cumulative (Weibull) Distribution have been used for calculating average load demand on monthly as well as daily basis. From the results we can conclude that Beta Density Function is a better technique than Arithmetic Mean and Weibull Density Function. This analysis will be helpful for future generation plann ing.

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A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

ABSTRACT The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The galloping climate change and the expected running out of fossil fuels turns the use of such non-polluting cars into a priority for most developed countries. However, such a use has led to major concerns to power companies, since they must adapt their generation to a new scenario, in which electric vehicles will dramatically modify the curve of generation. In this paper, a novel approach based on ensemble learning is proposed. In particular, ARIMA, GARCH and PSF algorithms’ performances are used to forecast the electric vehicle power consumption in Spain. It is worth noting that the studied time series of consumption is non-stationary and adds difficulties to the forecasting process. Thus, an ensemble is proposed by dynamically weighting all algorithms over time. The proposal presented has been implemented for a real case, in particular, at the Spanish Control Centre for the Electric Vehicle. The performance of the approach is assessed by means of WAPE, showing robust and promising results for this research field.
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Forecasting the Demand of Short Term Electric Power Load with Large Scale LP SVR

Forecasting the Demand of Short Term Electric Power Load with Large Scale LP SVR

This research studies short-term electricity load prediction with a large-scalelinear programming support vector regres- sion (LP-SVR) model. The LP-SVR is compared with other three non-linear regression models: Collobert’s SVR, Feed- Forward Neural Networks (FFNN), and Bagged Regression Trees (BRT). The four models are trained to predict hourly day-ahead loads given temperature predictions, holiday information and historical loads. The models are trained on- hourly data from the New England Power Pool (NEPOOL) region from 2004 to 2007 and tested on out-of-sample data from 2008. Experimental results indicate that the proposed LP-SVR method gives the smallest error when compared against the other approaches. The LP-SVR shows a mean absolute percent error of 1.58% while the FFNN approach has a 1.61%. Similarly, the FFNN method shows a 330 MWh (Megawatts-hour) mean absolute error, whereas the LP-SVR approach gives a 238 MWh mean absolute error. This is a significant difference in terms of the extra power that would need to be produced if FFNN was used. The proposed LP-SVR model can be utilized for predicting power loads to a very low error, and it is comparable to FFNN and over-performs other state of the art methods such as: Bagged Regres- sion Trees, and Large-Scale SVRs.
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Research on Distributed Power Supply Consumption Method Combined with Electric Heating System

Research on Distributed Power Supply Consumption Method Combined with Electric Heating System

In terms of participation mode, first of all, the paper determines the controllable load resources and types through users’ energy diagnosis, which can be divided into adjustable and interruptible control types in detail, summarizes the load data by installing and controlling the measurement terminal, and makes use of big data intelligent analysis methods to realize the multi-time scale power consumption analysis on the demand side.

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Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems

Forecasting Models for Integration of Large-Scale Renewable Energy Generation to Electric Power Systems

To solve the optimization problem in Eq. (2-13), 𝛾 and 𝜎, which are called hyperparameters, should be selected through optimization before training the LSSVM. In this regard, a coupled simulated annealing (CSA) [71] is first performed to find a starting point for the simplex algorithm, and then simplex is performed as a fine-tuning step. Employing CSA-simplex in tuning regularization and kernel function parameters in LSSVM has been successfully achieved (e.g., [72]). Notably, to make optimization of hyper-parameters robust, cross-validation (CV) [69] is employed herein. CV is a statistical technique to prevent over-fitting as well as the dependency on datasets for finding parameters. In this paper, k-fold CV is used and the original training set is randomly divided into k equal-sized subsets. A subset is chosen as a validation set and the remainder are used as the training set. The LSSVM is then trained, and 𝒘 and b, which suitably fit the LSSVM to the training set, are calculated by using different combinations of hyperparameters based on the CSA. For each chosen combination of hyper-parameters, the trained LSSVM is evaluated with the validation set by means of an evaluation index (such as mean square error or mean absolute error). The process is repeated k times, with each of the subsets used once as the validation set. The average value of the evaluation index is calculated. The CSA process is repeated until the predetermined acceptable evaluation index value is satisfied. The optimized hyperparameters are then used as the starting point for simplex for fine-tuning the hyper- parameters. After the hyper-parameters are determined, Eq. (2-13) can be simply solved by a Karush-Kuhn-Tucker approach to find 𝒘 and b.
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Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review

Different Methods of Long-Term Electric Load Demand Forecasting a Comprehensive Review

Long-term load forecasting represents the first step in developing future generation, transmission, and distribution facilities. Any substantial deviation in the forecast, particularly under the new market structure, will result in either overbuilding of supply facilities, or curtailment of customer demand. The confidence levels associated with classical forecasting techniques, when applied to forecasting problem in mature and stable utilities are unlikely to be similar to those of dynamic and fast growing utilities. This is attributed to the differences in the nature of growth, socio-economic conditions, occurrence of special events, extreme climatic conditions, and the competition in generation due to the deregulation of the electricity sector with possible changes in tariff structures. Under such conditions, these forecasting techniques are insufficient to establish demand forecast for long-term power system planning. Consequently, this case requires separate consideration either by pursuing the search for more improvement in the existing forecasting techniques or establishing another approach to address the forecasting problem of such systems.
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Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

A Particle swarm optimization PSO technique is used to find the optimal para- meters for different forecasting methods. This algorithm is used to solve a wide class of complex optimization problems in engineering and science. Both linear and nonlinear models will be used in the system and the results will be obtained using PSO. Through the implementation of PSO all particles are kept as mem- bers of the population. The basic idea of the PSO is the mathematical modeling and simulation of the food searching activities of a swarm of birds in the multi- dimensional space where the optimal solution is sought. Each particle in the swarm is moved towards a point where it obtains optimal solution by the influ- ence of its velocity. The velocity of a particle is affected by three factors; inertial momentum, cognitive and social [11]. The goal of PSO is to find the optimal va- riable values for a certain function. Each particle knows its optimal value ( p best )
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Short Term Electric Load Forecasting.

Short Term Electric Load Forecasting.

impact varies on different economical conditions. For instance, during the year of 2009, which is the early part of a recession in US, the energy consumption of US is lower than that of 2008, because people were using power more conservatively, and lots of businesses were closed. With the advancement of econometric techniques, the economics information can be relatively accurate up to one year ahead, and be inaccurate but reliable up to 3 years ahead for load forecasting purpose. In the annual resolution, both climate and economics can affect the energy consumption. However, due to the unavailability of both inputs, the system level load forecast can be only obtained by simulating various scenarios. On the other hand, the long term energy consumption on the circuit level is affected by urban development, which can be realized by land use changes. The land use information is normally accurate within one year, inaccurate but reliable up to 5 years. Although some counties can provide a 30 years ahead urban development plan, it is still not clear what exactly would happen year by year during the next 30 years. Forecasting the load on the circuit level with land use simulation is called spatial electric load forecasting [89].
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An Intelligent System For Forecasting Electric
          Load And Price

An Intelligent System For Forecasting Electric Load And Price

In India consumption of electricity increasing approximately 10-11% per year. A proper management and control on electric load is very necessary for this purpose most important factor is an efficient forecasting system of electric load is required. Load forecasting has always been important for organizing, planning and operation decision. Forecasting can be of any type like – long term, medium term & short term. Sort term forecasting is used to forecasting the load of within week, mid term forecasting is used for more than a week and less than a year and Long term forecasting is used for more than a year. Several operations of load like real time control on load generation, spinning reserve allocation, security analysis, load interchanges with other utilities, temperature variation analysis and planning of energy transactions are based on short term load forecasting. It is very essential to forecast the load correctly in the power system because errors in load forecast results suboptimal unit commitment decisions. Relationship between electric load and its derived factors make it complex and non-linear which yields difficulties to process it with traditional techniques line linear and multi regression model auto regression [1], autoregressive moving average (ARMA), exponential
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Statistical and Simulation analysis of Small Wind Power Forecasting for House hold applications – A Case Study

Statistical and Simulation analysis of Small Wind Power Forecasting for House hold applications – A Case Study

The rapid increase in the population worldwide adversely reports in increase in the power demand and it has also resulted in the fast rate of depletion of fossil fuel reserves .At the same time the awareness in environmental degradation has given rise to the use of alternative approach the renewable source of energy like solar, hydro, geothermal, tidal and bio-energy. Out of these above mentioned clean energy sources Wind energy conversion has emerged as a boon in the recent years. The harnessing or trapping of wind power has become easier due to the recent advancement in the technologies with a cost effective plan which challenges the traditional methods. The wind power utilization is the answer to all the problems which are being encountered
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Solar Power Forecasting

Solar Power Forecasting

In contrast, the direct group of methods directly predict the output of the PV power systems, without the need to firstly predict the solar irradiance. The main data source is the previous PV power data which is readily available, and the additional data sources include historical weather data and weather forecasts for the new days. This weather information is less complex and easily available for the location of the PV plant than the information required by the indirect methods. Also, the additional data sources can be used to improve the accuracy compared to only using the historical PV power data, but they are no longer indispensable. This enables the wider application of di- rect approaches, compared to the direct ones. The direct approaches can be further divided into two groups, namely statistical and machine learning methods. The former are based on statistical models such as Autoregressive Moving Average (ARMA), Au- toregressive Integrated Moving Average (ARIMA) and Exponential Smoothing (ES) [7, 8, 9, 10, 11]. The latter group applies machine learning algorithms such as Neural Networks(NN)[12, 13, 14], Support Vector Regression (SVR)[15], k Nearest Neigh- bors (k-NN) [16, 7].
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Study on Communication Methods for Electric Power High-voltage Equipment Monitoring System

Study on Communication Methods for Electric Power High-voltage Equipment Monitoring System

In the software design, the data collection algorithm of high voltage equipment monitoring system is optimized, and fuzzy comprehensive algorithm and opportunis- tic routing algorithm are applied. As data is being transferred, data thresholds are added to the program in the sensor acquisition node, reducing the amount of redun- dant data sent. In the design of the coordinator node software, the fuzzy comprehen- sive evaluation algorithm is applied to the data fusion, which realizes the preliminary processing of the data and reduces the amount of data sent to the sink node. The role of data processing is to reduce the energy consumption of the node, extending the node's life cycle. In the process of data upload, a routing algorithm based on the shortest distance and residual energy is designed to improve the network throughput and extend the network life cycle.
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Advanced forecasting methods for renewable generation and loads in modern power systems

Advanced forecasting methods for renewable generation and loads in modern power systems

Measurements were taken for the total load, loads of each feeder, and single loads (such as individual machines, the office building, electrical pumps, and robots). The measurement system included 19 measurement points recording the data of the average active and reactive powers, currents, voltages, frequency, and power factor in 15- minute intervals. These values were then averaged in groups of four to provide hourly measurements from April 1, 2016 to July 31, 2017. Fig. 3.1 shows typical profiles of the total active and reactive powers during a 7-day period (from May 2, 2016 to May 8, 2016). Statistical parameters of the four load time series (i.e., of the aggregate load, an electrical pump, the carpentry feeder, and a painting machine) considered in the following applications in Section 3.3.2 are shown in Table 3.1.
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Modeling and Forecasting Electric Vehicle Consumption Profiles

Modeling and Forecasting Electric Vehicle Consumption Profiles

The car stock of electric vehicles (EVs)—electric battery and plug-in hybrids—reached 2 million units worldwide in 2016, accounting for 1.1% of the global car market share [1]. This share is expected to rapidly increase over the next 15 years. Charging an EV battery requires a large amount of energy in a small amount of time. In a typical US household, EV charging requires more power than any other appliances (e.g., stoves and dryers) and is solicited just as often (daily or more), see Figure 1. EVs are therefore important appliances to model correctly in order to manage electric household consumption. The increasing number of EVs connected to the grid, coupled with their high power requirement, is challenging the current electrical network with higher overall consumption and additional peaks.
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Forecasting Intra-Hour Imbalances in Electric Power Systems

Forecasting Intra-Hour Imbalances in Electric Power Systems

The first publication (Garcia and Kirschen 2004) dis- cusses and shows the limitations and insufficiency of richly applied but basic forecasting techniques such as ARIMA and exponential smoothing, because of the non-periodic, non-stationary and noisy character of imbalance time-series. Instead, the contemporary state-of-the-art artificial neural networks (ANN) are applied to uncover the non-linearity and irregularity of the data, and predict the daily imbalance medians. Presented are improvements compared to methods based on a linear regression, and the fact that none of the neural networks provided optimal predictions for all mar- ket conditions is discussed. Two use cases were evaluated: prediction of daily medians with three months training and one month testing window, and prediction of six values for each day with four week training and one week testing win- dow. The following predictor variables were employed: de- mand forecast, demand forecast error, accepted bid volumes, accepted offer volumes, forward trades, gate closure imbal- ance volume, accepted offers and bids, imbalance prices and day of the week. However, it is not clear whether historical imbalances were utilized as one of the input features of the models.
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Power Load Management Techniques and Methods in Electric Power System

Power Load Management Techniques and Methods in Electric Power System

Weather sensitive loads are the targets of direct load control, of which air-conditioning (A/C) units and Water heaters (W/H's) were selected for cycling strategy. Although W/H's loads are not large they can influence load changes. Water pumping imposes a major influence on the electric supply system, and their loads have a notable effect on increasing system peak load.A survey of a small sample of A/C unit owners, which was done as part of this work, has shown an increase of electricity consumption in the summer months due to A/C load. An average of 8 hours/day of A/C units in service was recorded including peak hours.
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