Review of multiple load forecasting method for integrated energy system

In order to further improve the efficiency of energy utilization, Integrated Energy Systems (IES) connect various energy systems closer, which has become an important energy utilization mode in the process of energy transition. Because the complex and variable multiple load is an important part of the new power system, the load forecasting is of great significance for the planning, operation, control, and dispatching of the new power system. In order to timely track the latest research progress of the load forecasting method and grasp the current research hotspot and the direction of load forecasting, this paper reviews the relevant research content of the forecasting methods. Firstly, a brief overview of Integrated Energy Systems and load forecasting is provided. Secondly, traditional forecasting methods based on statistical analysis and intelligent forecasting methods based on machine learning are discussed in two directions to analyze the advantages, disadvantages, and applicability of different methods. Then, the results of Integrated Energy Systemss multiple load forecasting for the past 5 years are compiled and analyzed. Finally, the Integrated Energy Systems load forecasting is summarized and looked forward

Development and comparison of two computational intelligence algorithms for electrical load forecasts with multiple time scales

Electricity load forecasting provides the critical information required for power institutions and authorities to develop rational, effective, and economic dispatch plans. The load forecasting at the regional power system is important for optimal management and accommodating local renewable energy sources, which is a challenging task as the demand variations are more sensitive to local weather changes (such as temperature, humidity, precipitation, and wind speed) and consumers' activities and behaviours. The paper aims to develop a new prediction method using intelligent computational algorithms. Long Short-Term Memory (LSTM), a deep recurrent neural network, explores the long-term dependency of network memory sequence data to identify intrinsic variations in both horizontals (time series) and vertical (network depth) dimensions over a longer historical period. Support Vector Machine (SVM) is a typical learning method that has been successfully implemented to solve nonlinear regression and time series problems. This paper studies the two methods and adapts the two methods to become suitable algorithms for load prediction. The paper presents the algorithms, their applications and prediction results. The prediction performance is compared for using LSTM and SVM at ultra-short, short-term, medium-term, and long-term forecasting. The results show that LSTM has higher prediction accuracy than SVM in both ultra-short and short-term forecasts, but SVM is more capable of medium-term and long-term forecasting. Finally, the epoch time for LSTM and SVM is also calculated and compared

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

Exploiting road traffic data for very short term load forecasting in smart grids

If accurate short term prediction of electricity consumption is available, the Smart Grid infrastructure can rapidly and reliably react to changing conditions. The economic importance of accurate predictions justifies research for more complex forecasting algorithms. This paper proposes road traffic data as a new input dimension that can help improve very short term load forecasting. We explore the dependencies between power demand and road traffic data and evaluate the predictive power of the added dimension compared with other common features, such as historical load and temperature profiles

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.Comment: 10 pages, 5 figures, KES-IDT 2015 conference. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-19857-6_