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)

Forecast Based Consensus Control for DC Microgrids Using Distributed Long Short-Term Memory Deep Learning Models

In a microgrid, renewable energy sources (RES) exhibit stochastic behavior, which affects the microgrid continuous operation. Normally, energy storage systems (ESSs) are installed on the main branches of the microgrids to compensate for the load-supply mismatch. However, their state of charge (SoC) level needs to be balanced to guarantee the continuous operation of the microgrid in case of RES unavailability. This paper proposes a distributed forecast-based consensus control strategy for DC microgrids that balances the SoC levels of ESSs. By using the load-supply forecast of each branch, the microgrid operational continuity is increased while the voltage is stabilized. These objectives are achieved by prioritized (dis)charging of ESSs based on the RES availability and load forecast. Each branch controller integrates a load forecasting unit based on long short-term memory (LSTM) deep neural network that adaptively adjusts the (dis)charging rate of the ESSs to increase the microgrid endurability in the event of temporary generation insufficiencies. Furthermore, due to the large training data requirements of the LSTM models, distributed extended Kalman filter algorithm is used to improve the learning convergence time. The performance of the proposed strategy is evaluated on an experimental 380V DC microgrid hardware-in-the-loop test-bench and the results confirm the achievement of the controller objectives

A study of the accuracy, completeness, and efficiency of artificial neural networks and related inductive learning techniques

Artificial Neural Networks (ANNs) have been an intense topic of research in the last decade. They have been viewed as black boxes, where the inputs were known and the outputs were computed, but the underlying statistics and thus reliability of the networks were not fully understood. Because of this, there has been hesitation in utilizing ANNs in automated systems such as intelligent flight control. This hesitation is diminishing, however. Individual elements of a neural network can be probed and their decision-making power assessed. In this study, a neural network is trained and then various ranking methods are used to assess the importance (saliency or decision-making power, DMP) of each input node. Then, the input data is renormalized according to the DMP input vector and fed to a general regression neural network (GRNN) for training. The accuracy of the DMP ranking methods are then compared against each other from the resulting modified GRNNs. Five ranking methods are tested and compared on four separate data sets. A series of new methods are then introduced that combine the global nonlinear regression capability of ANNs with the local averaging capability of nearest neighbor approaches, based on a weighted distance metric (WDM) provided by the saliency estimates. Two new neural stacking methods are introduced that rely on this WDM. A framework for quantifying error estimation reliability is presented and discussed. Using this framework, the predictive accuracy of MSA and DCM are compared in terms of both the modeled target function and the model\u27s confidence interval about it using a new measure called the confidence coefficient. A benchmark problem is also introduced as a generic data set for future comparison between inductive learning machines. In addition, the Scaled Conjugate Gradient algorithm (SCG) is implemented for its potential in supervised learning. Two new complexity-regularization methods derived from SCG are implemented that use saliency estimates of various features of the ANN, and are driven by feedback from the cross validation (feedback) set

Textual-Knowledge-Guided Numerical Feature Discovery Method for Power Demand Forecasting

Power demand forecasting is a crucial and challenging task for new power system and integrated energy system. However, as public feature databases and the theoretical mechanism of power demand changes are unavailable, the known features of power demand fluctuation are much limited. Recently, multimodal learning approaches have shown great vitality in machine learning and AIGC. In this paper, we interact two modal data and propose a textual-knowledge-guided numerical feature discovery (TKNFD) method for short-term power demand forecasting. TKNFD extensively accumulates qualitative textual knowledge, expands it into a candidate feature-type set, collects numerical data of these features, and eventually builds four-dimensional multivariate source-tracking databases (4DM-STDs). Next, TKNFD presents a two-level quantitative feature identification strategy independent of forecasting models, finds 43-48 features, and systematically analyses feature contribution and dependency correlation. Benchmark experiments in two different regions around the world demonstrate that the forecasting accuracy of TKNFD-discovered features reliably outperforms that of SoTA feature schemes by 16.84% to 36.36% MAPE. In particular, TKNFD reveals many unknown features, especially several dominant features in the unknown energy and astronomical dimensions, which extend the knowledge on the origin of strong randomness and non-linearity in power demand fluctuation. Besides, 4DM-STDs can serve as public baseline databases.Comment: 12 pages, 12 figure

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

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

Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations

The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.Comment: 37 pages, 6 figures, 2 tables, review pape