Prediction the data consumption for power demands by elman neural network

The load forecasting consider as part important in power system operation.  The exact prediction for power demand is important for planning how much need extra power generation to cover extra load to keep without happen shutdown. Neural networks stay frequently designed for modeling dynamic processes. The Multi-Layer Perceptron (MLP) with Radial Basis Functions (RBF) network is static approximations used fewer frequently in the discrete-time domain. In this paper proposed predict method for daily peak load by Elman Neural Network (ENN) with using data power demand for 2 years collected from National Control Center (NCC) and comparing the result. The result show the proposal is evaluated and followed the power demand

PV Output forecasting based on weather classification, SVM and ANN

211-217The expansion in solar power is expected to be dramatic soon. A number of solar parks with high capacities are being setup to harness the potential of this renewable resource. However, the variability of solar power remains an important issue for grid integration of solar PV power plants. Changing weather conditions have affected the PV output. Thus, developing methods for accurately forecasting solar PV output is essential for enabling large-scale PV deployment. This paper has proposed a model for forecasting PV output based on weather classification, using a solar PV plant in Maharashtra, India, as the sample system. The input data is first classified using RBF-SVM (Radial Basis Function Support Vector Machines) into three types based on weather conditions, namely, sunny, rainy and cloudy. Then, the neural network model corresponding to that weather type has been applied to forecast the solar PV output. The obtained results for the overall model is studied for its effectiveness and are compared with existing research

Big Data Analysis application in the renewable energy market: wind power

Entre as enerxías renovables, a enerxía eólica e unha das tecnoloxías mundiais de rápido crecemento. Non obstante, esta incerteza debería minimizarse para programar e xestionar mellor os activos de xeración tradicionais para compensar a falta de electricidade nas redes electricas. A aparición de técnicas baseadas en datos ou aprendizaxe automática deu a capacidade de proporcionar predicións espaciais e temporais de alta resolución da velocidade e potencia do vento. Neste traballo desenvólvense tres modelos diferentes de ANN, abordando tres grandes problemas na predición de series de datos con esta técnica: garantía de calidade de datos e imputación de datos non válidos, asignación de hiperparámetros e selección de funcións. Os modelos desenvolvidos baséanse en técnicas de agrupación, optimización e procesamento de sinais para proporcionar predicións de velocidade e potencia do vento a curto e medio prazo (de minutos a horas)

Optimal planning and sizing of an autonomous hybrid energy system using multi stage grey wolf optimization

The continuous increase in energy demand and the perpetual dwindling of fossil fuel coupled with its environmental impact have recently attracted research focus in harnessing renewable energy sources (RES) across the globe. Representing the largest RES, solar and wind energy systems are expanding due to the growing evidence of global warming phenomena. However, variability and intermittency are some of the main features that characterize these RES as a result of fluctuation in weather conditions. Hybridization of multiple sources improves the system’s efficiency and reliability of supply due to the varying nature of the RES. Also, the unavailability of solar radiation (SR) and wind speed (WS) measuring equipment in the meteorological stations necessitates the development of prediction algorithms based on Artificial Intelligent (AI) techniques. This thesis presents an autonomous hybrid renewable energy system for a remote community. The hybrid energy system comprises of a photovoltaic module and wind turbine as the main source of energy. Batteries are used as the energy storage devices and diesel generator as a backup energy supply. A new hybrid Wavelet Transform and Adaptive Neuro-Fuzzy Inference System (WT-ANFIS) is developed for the SR prediction, while a hybrid Particle Swarm Optimization (PSO) and ANFIS (PSO-ANFIS) algorithm is developed for the WS prediction. The prediction accuracy of the proposed WT-ANFIS model was validated by comparison with the conventional ANFIS model, Genetic Algorithm (GA) and ANFIS (GA-ANFIS), and PSO-ANFIS models. The proposed PSO-ANFIS for the WS prediction is also compared with ANFIS and GA-ANFIS models. Also, Root Mean Square Error (RMSE), Correlation Coefficient (r) and Coefficient of Determination (R²) are used as statistical indicators to evaluate the performance of the developed prediction models. Additionally, a techno-economic feasibility analysis is carried out using the SR and WS data predicted to assess the viability of the hybrid solar-wind-battery-diesel system for electricity generation in the selected study area. Finally, a new cost-effective Multi Stage – Grey Wolf Optimization (MS-GWO) algorithm is applied to optimally size the different system components. This is aimed at minimizing the net present cost (NPC) while considering reliability and satisfying the load demand. MS-GWO is evaluated by comparison with PSO, GWO and PSO-GWO algorithms. From the results obtained, the statistical evaluators used for model performance assessment of the SR prediction shows that the hybrid WT-ANFIS model’s accuracy outperforms the PSO-ANFIS model by 65% RMSE and 9% R². Also, from the simulation results, the optimal configuration has an NPC of $1.01 million and cost of energy (COE)$0.110/kWh, with an operating cost of $4,723. The system is environmentally friendly with a renewable fraction of 98.3% and greenhouse gas emission reduction of 65%. Finally, a comparison is done between the proposed MS-GWO algorithm with the PSO, GWO and PSO-GWO algorithms. Based on this comparison, the proposed hybrid MS-GWO algorithm outperforms the individual PSO, GWO and PSO-GWO by 3.17%, 2.53% and 2.11% in terms of NPC and reduces the computational time by 53%, 46% and 36% respectively. Therefore, it can be concluded that the proposed MS-GWO technique can be applied for optimal sizing application globally

Advanced local prediction and its applications in power and energy systems

Due to the global energy crisis and environmental concerns, the development of sustainable energy is considered by more and more countries. In order to make this target, energy demand management is significantly necessary in which forecasting the energy demand is the starting point. The accurate prediction of energy demand could help the energy sectors to make these operation decisions and policy properly. A novel approach, which is the support vector regression based local predictor with false neighbor filtered (FNF-SVRLP), is proposed. This method is an amelioration of the support vector regression based local predictor (SVRLP). SVRLP is a powerful prediction method which employs phase reconstruction algorithms, such as the correlation dimension and mutual information methods used in time series analysis for data preprocessing. Compared with the global prediction method, in a local prediction method, each predicting point has its own model constructed based on its nearest neighbors (NNs) reconstructed from the time series, and the fitness of NNs would mainly affect the model performance. However, it has been found that NNs may contain a class of false neighbors (FNs) which would decrease the fitting accuracy dramatically and lead to a poorer forecasting performance. Therefore, a new false neighbor filter is proposed to remove those false neighbors and keep the optimal nearest neighbors. Then, the FNF-SVRLP is proposed. Wind power is one of the most popular renewable energy. The increasing penetration of wind power into the electric power grid accompanied with a series of challenges. Due to the uncertain and variable nature of wind resources, the output power of wind farms is hard to control, which could lead to the instability of the power grid operation and the unreliability of electricity supplies. In order to slove this problem, the FNF-SVRLP based short-term wind power perdition model is presented. Through the comparison with the SVRLP based short-term wind power perdition and ARMA based short-term wind power perdition, it is found that the FNF-SVRLP based short-term wind power perdition model is much more accurate than the others. Due to the fact that natural gas is cleanest burning of all fossil fuel, it can be considered as an important adjunct to renewable energy sources such as wind or solar, as well as a bridge to the new energy economy. Different from the wind power, the customer consumption behavior could effect the natural gas demand. Therefore, the customer behavior based ``Advanced Model" with FNF-SVRLP is presented to undertake the natural gas prediction. The proposed FNF-SVRLP natural gas model is compared with the SVRLP and autoregressive moving average (ARMA) to show its superiority. In addition, a web sever based online natural gas demand perdition system has been set up to help the National Grid to obtain the accurate daily natural gas demand perdition easily and timely. It is found that the most kinds of energy demand data are non-stationary, the internal regularity between predicting point and its nearest-neighbors are much more complex than the stationary dataset. In order to help the local predictor to capture the internal regularity between predicting point and its nearest-neighbors more accurately, the morphological filter is proposed. the morphological filter is applied to decompose the non-stationary dataset into several subsequences, ranked form the low frequency subsequence to the high frequency subsequence. Through this way, the local predictor could capture the non-stationary dataset more accurate, and improve the final performance of prediction. The morphological filter is applied to decompose the non-stationary into several subsequences, ranked form the low frequency subsequence to the high frequency subsequence. Through this way, the local predictor could capture the non-stationary dataset more accurate, and improve the final performance of prediction. Moveover, an novel calculation method of structure element (SE) is introduced. Different form the conventional SE, this novel approach can optimize the scale and shape of SE to match the original signal. After that, a novel algorithm, which is mathematical morphology based local prediction with support vector regression (SVRLP-MM) is proposed. The real-world wind speed data has been used to evaluate the performance of SVRLP-MM. The final results presented demonstrate that SVRLP-MM based wind speed prediction model can achieve a higher prediction accuracy than the SVRLP based model and ARMA model based model by using the same real-world wind speed data

Smart Energy Management for Smart Grids

This book is a contribution from the authors, to share solutions for a better and sustainable power grid. Renewable energy, smart grid security and smart energy management are the main topics discussed in this book

Solar Power Forecasting

Solar energy is a promising environmentally-friendly energy source. Yet its variability affects negatively the large-scale integration into the electricity grid and therefore accurate forecasting of the power generated by PV systems is needed. The objective of this thesis is to explore the possibility of using machine learning methods to accurately predict solar power. We first explored the potential of instance-based methods and proposed two new methods: the data source weighted nearest neighbour (DWkNN) and the extended Pattern Sequence Forecasting (PSF) algorithms. DWkNN uses multiple data sources and considers their importance by learning the best weights based on previous data. PSF1 and PSF2 extended the standard PSF algorithm deal with data from multiple related time series. Then, we proposed two clustering-based methods for PV power prediction: direct and pair patterns. We used clustering to partition the days into groups with similar weather characteristics and then created a separate PV power prediction model for each group. The direct clustering groups the days based on their weather profiles, while the pair patterns consider the weather type transition between two consecutive days. We also investigated ensemble methods and proposed static and dynamic ensembles of neural networks. We first proposed three strategies for creating static ensembles based on random example and feature sampling, as well as four strategies for creating dynamic ensembles by adaptively updating the weights of the ensemble members based on past performance. We then explored the use of meta-learning to further improve the performance of the dynamic ensembles. The methods proposed in this thesis can be used by PV plant and electricity market operators for decision making, improving the utilisation of the generated PV power, planning maintenance and also facilitating the large-scale integration of PV power in the electricity grid

A scheduling model for the charging of electric vehicles in photovoltaic powered smart microgrids

Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs

A scheduling model for the charging of electric vehicles in photovoltaic powered smart microgrids

Electric vehicles (EVs) have emerged as a viable option to advance sustainable mobility, but adoption is still relatively low. This has been largely due to the limited range one can travel on a single charge, leading to range anxiety, longer charge cycles and long wait times at charging stations. One solution to range anxiety is to erect charging stations on major roads and urban centres. There is also a lack of real-time information regarding the state of charging stations and charging ports in existing charging infrastructure. To increase the benefit of using EVs, using renewable energy sources, such as photovoltaics (PV) to power EVs, can further increase the benefit of reduced carbon footprint. The main research objective was to design a Charge Scheduling Model for charging EVs using a PV-powered smart microgrid (SMG). The model addresses the lack of an integrated platform where EV drivers can schedule when and where to charge their EVs. The model also reduces the negative effects of the adoption of EVs, including range anxiety. The Charge Scheduling Model was developed using the Design Science Research (DSR) methodology and was the main artefact of the study. A literature study was conducted of research related to SMGs, renewable energy, EVs and scheduling, to identify shortcomings that currently exist in EV charge scheduling (EVCS), and to identify the requirements of a potential solution. The literature study also identified the hard and soft constraints that are unique to EVCS, and the available energy in the SMG was identified as one of the hard constraints. Therefore, an Energy Forecasting Model for forecasting energy generated in PV-powered SMGs was required before the Charge Scheduling Model could be designed. During the first iteration of the design and development activities of DSR, four models were designed and implemented to evaluate their effectiveness in forecasting the energy generated in PV-powered SMGs. The models were Support Vector Regression (SVR), K-Nearest Neighbour (KNN), Decision Trees, and Multilayer Perceptron. In the second iteration, the Charge Scheduling Model was designed, consisting of a Four Layered Architecture and the Three-Phase Data Flow Process. The Charge Scheduling Model was then used to design the EVCS prototype. The implementation of the EVCS prototype followed the incremental prototyping approach, which was used to verify the effectiveness of the model. An artificial-summative evaluation was used to evaluate the design of the Charge Scheduling Model, whereas iterative formative evaluations were conducted during the development of the EVCS prototype. The theoretical contribution of this study is the Charge Scheduling Model, and the EVCS prototype is the practical contribution. The results from both evaluations, i.e. the Energy Forecasting Model and the Charge Scheduling Model, also make a contribution to the body of knowledge of EVs