Deep Learning Based Load Forecasting with Decomposition and Feature Selection Techniques

505-517The forecasting of short term electricity load plays a vital role in power system. It is essential for the power system's reliable, secure, and cost-effective functioning. This paper contributes significantly for enhancing the accuracy of short term electricity load forecasting. It presents a hybrid forecasting model called Gated Recurrent Unit with Ensemble Empirical Mode Decomposition and Boruta feature selection (EBGRU). It is a hybrid model that addresses the non-stationary, non-linearity and noisy issues of the time series input by using Ensemble Empirical Mode Decomposition (EEMD). It also addresses overfitting and curse of dimensionality issues of load forecasting by identifying the pertinent features using Boruta wrapper feature selection. It effectively handles the uncertainty and temporal dependency characteristics of load and forecasts the future load using deep learning based Gated Recurrent Unit (GRU). The proposed EBGRU model is experimented by using European and Australian Electricity load datasets. The temperature has high correlation with load demand. In this study, both load and temperature features are considered for the accurate short term load forecasting. The experimental outcome demonstrates that the proposed EBGRU model outperforms other deep learning models such as RNN, LSTM, GRU, RNN with EEMD and Boruta (EBRNN) and LSTM with EEMD and Boruta (EBLSTM)

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Energy Analytics for Infrastructure: An Application to Institutional Buildings

abstract: Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the ‘Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework. The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to 1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques. 2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms. 3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms. With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

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)

Development of Neurofuzzy Architectures for Electricity Price Forecasting

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision‐making process as well as strategic planning. In this study, a prototype asymmetric‐based neuro‐fuzzy network (AGFINN) architecture has been implemented for short‐term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well‐established learning‐based models

Optimized hybrid ensemble learning approaches applied to very short-term load forecasting

The significance of accurate short-term load forecasting (STLF) for modern power systems’ efficient and secure operation is paramount. This task is intricate due to cyclicity, non-stationarity, seasonality, and nonlinear power consumption time series data characteristics. The rise of data accessibility in the power industry has paved the way for machine learning (ML) models, which show the potential to enhance STLF accuracy. This paper presents a novel hybrid ML model combining Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGBoost), k-Nearest Neighbors (kNN), and Support Vector Regression (SVR), examining both standalone and integrated, coupled with signal decomposition techniques like STL, EMD, EEMD, CEEMDAN, and EWT. Through Automated Machine Learning (AutoML), these models are integrated and their hyperparameters optimized, predicting each load signal component using data from two sources: The National Operator of Electric System (ONS) and the Independent System Operators New England (ISO-NE), boosting prediction capacity. For the 2019 ONS dataset, combining EWT and XGBoost yielded the best results for very short-term load forecasting (VSTLF) with an RMSE of 1,931.8 MW, MAE of 1,564.9 MW, and MAPE of 2.54%. These findings highlight the necessity for diverse approaches to each VSTLF problem, emphasizing the adaptability and strength of ML models combined with signal decomposition techniques