Multi-directional gated recurrent unit and convolutional neural network for load and energy forecasting: A novel hybridization

Energy operations and schedules are significantly impacted by load and energy forecasting systems. An effective system is a requirement for a sustainable and equitable environment. Additionally, a trustworthy forecasting management system enhances the resilience of power systems by cutting power and load-forecast flaws. However, due to the numerous inherent nonlinear properties of huge and diverse data, the classical statistical methodology cannot appropriately learn this non-linearity in data. Energy systems can appropriately evaluate data and regulate energy consumption because of advanced techniques. In comparison to machine learning, deep learning techniques have lately been used to predict energy consumption as well as to learn long-term dependencies. In this work, a fusion of novel multi-directional gated recurrent unit (MD-GRU) with convolutional neural network (CNN) using global average pooling (GAP) as hybridization is being proposed for load and energy forecasting. The spatial and temporal aspects, along with the high dimensionality of the data, are addressed by employing the capabilities of MD-GRU and CNN integration. The obtained results are compared to baseline algorithms including CNN, Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). The experimental findings indicate that the proposed approach surpasses conventional approaches in terms of accuracy, Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RSME).</p> </abstract&gt

Machine Learning for Load Profile Data Analytics and Short-term Load Forecasting

Short-term load forecasting (STLF) is a key issue for the operation and dispatch of day ahead energy market. It is a prerequisite for the economic operation of power systems and the basis of dispatching and making startup-shutdown plans, which plays a key role in the automatic control of power systems. Accurate power load forecasting not only help users choose a more appropriate electricity consumption scheme and reduces a lot of electric cost expenditure but also is conducive to optimizing the resources of power systems. This advantage helps while improving equipment utilization for reducing the production cost and improving the economic benefit, and improving power supply capability. Therefore, ultimately achieving the aim of efficient demand response program. This thesis outlines some machine learning based data driven models for STLF in smart grid. It also presents different policies and current statuses as well as future research direction for developing new STLF models. This thesis outlines three projects for load profile data analytics and machine learning based STLF models. First project is, load profile classification and determining load demand variability with the aim to estimate the load demand of a customer. In this project load profile data collected from smart meter are classified using recently developed extended nearest neighbor (ENN) algorithm. Here we have calculated generalized class wise statistics which will give the idea of load demand variability of a customer. Finally the load demand of a particular customer is estimated based on generalized class wise statistics, maximum load demand and minimum load demand. In the second project, a composite ENN model is proposed for STLF. The ENN model is proposed to improve the performance of k-nearest neighbor (kNN) algorithm based STLF models. In this project we have developed three individual models to process weather data i.e., temperature, social variables, and load demand data. The load demand is predicted separately for different input variables. Finally the load demand is forecasted from the weighted average of three models. The weights are determined based on the change in generalized class wise statistics. This projects provides a significant improvement in the performance of load forecasting accuracy compared to kNN based models. In the third project, an advanced data driven model is developed. Here, we have proposed a novel hybrid load forecasting model based on novel signal decomposition and correlation analysis. The hybrid model consists of improved empirical mode decomposition, T-Copula based correlation analysis. Finally we have employed deep belief network for making load demand forecasting. The results are compared with previous studies and it is evident that there is a significant improvement in mean absolute percentage error (MAPE) and root mean square error (RMSE)

Improved EMD-Based Complex Prediction Model for Wind Power Forecasting

As a response to rapidly increasing penetration of wind power generation in modern electric power grids, accurate prediction models are crucial to deal with the associated uncertainties. Due to the highly volatile and chaotic nature of wind power, employing complex intelligent prediction tools is necessary. Accordingly, this article proposes a novel improved version of empirical mode decomposition (IEMD) to decompose wind measurements. The decomposed signal is provided as input to a hybrid forecasting model built on a bagging neural network (BaNN) combined with K-means clustering. Moreover, a new intelligent optimization method named ChB-SSO is applied to automatically tune the BaNN parameters. The performance of the proposed forecasting framework is tested using different seasonal subsets of real-world wind farm case studies (Alberta and Sotavento) through a comprehensive comparative analysis against other well-known prediction strategies. Furthermore, to analyze the effectiveness of the proposed framework, different forecast horizons have been considered in different test cases. Several error assessment criteria were used and the obtained results demonstrate the superiority of the proposed method for wind forecasting compared to other methods for all test cases.© 2020 Institute of Electrical and Electronics Engineersfi=vertaisarvioitu|en=peerReviewed

A secondary decomposition based hybrid structure with meteorological analysis for deterministic and probabilistic wind speed forecasting

Abstract(#br)Accurate wind speed forecasting could ensure the reliability and controllability for the wind power system. In this paper, a new hybrid structure based on meteorological analysis is proposed for the wind speed vector (wind speed and direction) deterministic and probabilistic forecasting. Twelve kinds of secondary decomposition methods are employed to decrease the interference existing in the data. To improve the training efficiency and accelerate the sample selection process, active learning is employed. Four different wind speed datasets collected from Ontario Province, Canada, are utilized as case studies to evaluate the forecasting performance of the proposed structure. Experimental results show that the proposed structure based on meteorological analysis is suitable for wind speed vector forecasting and could obtain better forecasting performance. Furthermore, except accurate deterministic forecasts, the proposed structure also provides more probabilistic forecasting information

A hybrid PSO-ANFIS approach for horizontal solar radiation prediction in Nigeria

For efficient and reliable hydrogen production via solar photovoltaic system, it is important to obtain accurate solar radiation data. Though there are equipment specifically designed for solar radiation prediction but are very expensive and have high maintenance cost that most countries like Nigeria are unable to purchase. In this study, the accuracy of a hybrid PSO-ANFIS method is examined to predict horizontal solar radiation in Nigeria. The prediction is done based on the available meteorological data obtained from NIMET Nigeria. The meteorological data used for this study are monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours, which serves as inputs to the developed model. The model accuracy is evaluated using two statistical indicators Root Mean Square Error (RMSE) and Coefficient of determination (R²). The accuracy of the proposed model is validated using ANFIS, GA-ANFIS models and other literatures. Based on the statistical parameters used for the model evaluation, the results obtained proves PSO-ANFIS as a good model for predicting solar radiation with the values of RMSE=0.68318, R²=0.9065 at the training stage and RMSE=1.3838, R²=0.8058 at the testing stage. This proves the potentiality of PSO-ANFIS technique for accurate solar radiation prediction

Short-term forecasting of electricity consumption using Gaussian processes

Forecasting of electricity consumption is considered as one of the most signi cant aspect of e ective management of power systems. On a long term basis, it allows decision makers of a power supplying company to decide when to build new power plants, transmission and distri- bution networks. On a short term basis, it can be used to allocate resources in a power grid to supply the demand continuously. Forecasting is basically divided into three categories : short-term, medium-term, and long- term. Short-term refers to an hour to a week forecast, while medium-term refers to a week to a year, and predictions that run more than a year refers to long-term. In this thesis, we forecast electricity consumption on a short-term basis for a particular region in Norway using a relatively novel approach: Gaussian process. We design the best feature vector suitable for forecasting electricity consumption using various factors such as previous consumptions, temperature, days of the week and hour of the day. Moreover, feature space is scaled and reduced using reduction and normalization methods, and di erent target variables are analysed to obtain better accuracy. Furthermore, GP is compared with two traditional forecasting techniques : Multiple Back- Propagation Neural Networks (MBPNN), and Multiple Linear Regression (MLR). Finally we show that GP is as better as MBPNN and far better than MLR using empirical results

Application of a novel early warning system based on fuzzy time series in urban air quality forecasting in China

© 2018 Elsevier B.V. With atmospheric environmental pollution becoming increasingly serious, developing an early warning system for air quality forecasting is vital to monitoring and controlling air quality. However, considering the large fluctuations in the concentration of pollutants, most previous studies have focused on enhancing accuracy, while few have addressed the stability and uncertainty analysis, which may lead to insufficient results. Therefore, a novel early warning system based on fuzzy time series was successfully developed that includes three modules: deterministic prediction module, uncertainty analysis module, and assessment module. In this system, a hybrid model combining the fuzzy time series forecasting technique and data reprocessing approaches was constructed to forecast the major air pollutants. Moreover, an uncertainty analysis was generated to further analyze and explore the uncertainties involved in future air quality forecasting. Finally, an assessment module proved the effectiveness of the developed model. The experimental results reveal that the proposed model outperforms the comparison models and baselines, and both the accuracy and the stability of the developed system are remarkable. Therefore, fuzzy logic is a better option in air quality forecasting and the developed system will be a useful tool for analyzing and monitoring air pollution