Short-Term Load Forecasting Utilizing a Combination Model: A Brief Review

To deliver electricity to customers safely and economically, power companies encounter numerous economic and technical challenges in their operations. Power flow analysis, planning, and control of power systems stand out among these issues. Over the last several years, one of the most developing study topics in this vital and demanding discipline has been electricity short-term load forecasting (STLF). Power system dispatching, emergency analysis, power flow analysis, planning, and maintenance all require it. This study emphasizes new research on long short-term memory (LSTM) algorithms related to particle swarm optimization (PSO) inside this area of short-term load forecasting. The paper presents an in-depth overview of hybrid networks that combine LSTM and PSO and have been effectively used for STLF. In the future, the integration of LSTM and PSO in the development of comprehensive prediction methods and techniques for multi-heterogeneous models is expected to offer significant opportunities. With an increased dataset, the utilization of advanced multi-models for comprehensive power load prediction is anticipated to achieve higher accuracy

Long-Term Electricity Load Forecasting Based On Cascade Forward Backpropagation Neural Network

Nowadays, the Electrical System has an important role in all sectors of life. Electricity has a strategic role. Accuracy and reliability in electricity load forecasting is a great key that can help electricity companies in supplying electricity efficiency, hence, reducing wasted energy. In addition, electricity load forecasting can also help electricity companies to determine the purchase price and power generation. Long-term forecasting is a method of forecasting with a span of more than one year. The historical data will be a reference in solving the problems. This research propose the concept of cascade forward backpropagation for long-term load forecasting. The advantage of this concept is that it can accommodate non-linear conditions without ignoring the linear conditions. This study compared the results of the original data, Feed Forward Backpropagation Neural Network (FFBNN) and Cascade Forward Backpropagation Neural Network (CFBNN). The results were measured by comparing Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers. This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy

Short term load forecasting with Markovian switching distributed deep belief networks

In modern power systems, centralised short term load forecasting (STLF) methods raise concern on high communication requirements and reliability when a central controller undertakes the processing of massive load data solely. As an alternative, distributed methods avoid the problems mentioned above, whilst the possible issues of cyberattacks and uncertain forecasting accuracy still exist. To address the two issues, a novel distributed deep belief networks (DDBN) with Markovian switching topology is proposed for an accurate STLF, based on a completely distributed framework. Without the central governor, the load dataset is separated and the model is trained locally, while obtaining the updates through communication with stochastic neighbours under a designed consensus procedure, and therefore significantly reduced the training time. The overall network reliability against cyberattacks is enhanced by continually switching communication topologies. In the meanwhile, to ensure that the distributed structure is still stable under such a varying topology, the consensus controller gain is delicately designed, and the convergence of the proposed algorithm is theoretically analysed via the Lyapunov function. Besides, restricted Boltzmann machines (RBM) based unsupervised learning is employed for DDBN initialisation and thereby guaranteeing the success rate of STLF model training. GEFCom 2017 competition and ISO New England load datasets are applied to validate the accuracy and effectiveness of the proposed method. Experiment results demonstrate that the proposed DDBN algorithm can enhance around 19% better forecasting accuracy than centralised DBN algorithm.</p

SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONU

SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONUÖzetSinir ağları algoritmalarından olan Aşırı Öğrenme Makinesi (AÖM)’de giriş ağırlığı ve gizli eşik değeri parametrelerinin rastgele seçilmekte ve çıktı katman ağırlıkları analitik olarak hesaplanmaktadır. Bundan dolayı ağın öğrenme işlemi hızlı bir şekilde gerçekleşmektedir. Ayrıca AÖM’nin gradyan temelli algoritmalara göre gizli katmanda ihtiyaç duyduğu nöron sayısı daha fazla olmaktadır. Bu nedenle giriş ağırlıkları ve gizli nöron eşik değerlerinin optimum değerlerinin bulunması AÖM'nin performansına etki etmektedir. Bu çalışmada bu optimum değerlerin belirlenmesinde sürü zekası algoritmalarından Parçacık Sürü Optimizasyonu (PSO) ve Rekabetçi Sürü İyileştirici (RSİ) kullanılmıştır. Optimum giriş ağırlıkları ve gizli eşik değerlerinin belirlenerek çıkış ağırlıkları Moore-Penrose genelleştirilmiş tersiyle analitik olarak hesaplanmıştır. AÖM, RSİ-AÖM ve PSO-AÖM modellerinin çok sınıflı tiroit veri setine uyarlanarak öğrenme parametrelerinin optimizasyonu ile en iyi doğruluk oranları sırasıyla %94.74, %94.86, %95.42 olarak elde edilmiştir. Optimizasyon metotlarının AÖM modellerinin sınıflandırma performansını artırdığı görülmüştür.Anahtar Kelimeler: Aşırı Öğrenme Makinesi (AÖM), Metasezgisel, Parçacık Sürü Optimizasyonu (PSO), Rekabetçi Sürü İyileştirici (RSİ)OPTIMIZATION OF LEARNING PARAMETERS OF EXTREME LEARNING MACHINE WITH SWARM INTELLIGENCE METHODSAbstractIn the Extreme Learning Machine (ELM), which is one of the neural networks algorithms, the input weight and hidden bias value parameters are randomly selected and the output layer weights are calculated analytically. Therefore, the learning process of the network takes place quickly. In addition, the number of neurons needed by the hidden layer is higher than the gradient-based algorithms. Finding optimum values of entry weights and hidden neuron bias values affects the performance of the ELM. In this study, Particle Swarm Optimization (PSO) and Competitive Swarm Optimizer (CSO) were used to determine these optimum values. By determining the optimum input weights and hidden bias values, the output weights were analytically calculated by Moore-Penrose generalized inverse. By adapting the multi-class thyroid data set of ELM, CSO-ELM and PSO-ELM models, the best accuracy rates were obtained as 94.74%, 94.86%, 95.42% respectively. It has been seen that optimization methods increase the classification performance of the ELM models.Keywords: Extreme Learning Machine (ELM), Metaheuristic, Particle Swarm Optimization (PSO), Competitive Swarm Optimizer (CSO

Internet of Things and Intelligent Technologies for Efficient Energy Management in a Smart Building Environment

Internet of Things (IoT) is attempting to transform modern buildings into energy efficient, smart, and connected buildings, by imparting capabilities such as real-time monitoring, situational awareness and intelligence, and intelligent control. Digitizing the modern day building environment using IoT improves asset visibility and generates energy savings. This dissertation provides a survey of the role, impact, and challenges and recommended solutions of IoT for smart buildings. It also presents an IoT-based solution to overcome the challenge of inefficient energy management in a smart building environment. The proposed solution consists of developing an Intelligent Computational Engine (ICE), composed of various IoT devices and technologies for efficient energy management in an IoT driven building environment. ICE’s capabilities viz. energy consumption prediction and optimized control of electric loads have been developed, deployed, and dispatched in the Real-Time Power and Intelligent Systems (RTPIS) laboratory, which serves as the IoT-driven building case study environment. Two energy consumption prediction models viz. exponential model and Elman recurrent neural network (RNN) model were developed and compared to determine the most accurate model for use in the development of ICE’s energy consumption prediction capability. ICE’s prediction model was developed in MATLAB using cellular computational network (CCN) technique, whereas the optimized control model was developed jointly in MATLAB and Metasys Building Automation System (BAS) using particle swarm optimization (PSO) algorithm and logic connector tool (LCT), respectively. It was demonstrated that the developed CCN-based energy consumption prediction model was highly accurate with low error % by comparing the predicted and the measured energy consumption data over a period of one week. The predicted energy consumption values generated from the CCN model served as a reference for the PSO algorithm to generate control parameters for the optimized control of the electric loads. The LCT model used these control parameters to regulate the electric loads to save energy (increase energy efficiency) without violating any operational constraints. Having ICE’s energy consumption prediction and optimized control of electric loads capabilities is extremely useful for efficient energy management as they ensure that sufficient energy is generated to meet the demands of the electric loads optimally at any time thereby reducing wasted energy due to excess generation. This, in turn, reduces carbon emissions and generates energy and cost savings. While the ICE was tested in a small case-study environment, it could be scaled to any smart building environment

Least squares support vector machine with self-organizing multiple kernel learning and sparsity

© 2018 In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand

Data-Intensive Computing in Smart Microgrids

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data). The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area