Recent Advances in Energy Time Series Forecasting

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published in MDPI’s Energies journal. The special issue took place in 2016 and accepted a total of 21 papers from twelve different countries. Electrical, solar, or wind energy forecasting were the most analyzed topics, introducing brand new methods with very sound results.Ministerio de Economía y Competitividad TIN2014-55894-C2-

An intelligent shell game optimization based energy consumption analytics model for smart metering data

Smart metering is a hot research topic and has gained significant attention since the electromechanical metering is not reliable and requires more energy and time. All the existing methods are focused only on how to deal with data rather than how to do efficiently. Prediction of electricity consumption is essential to gain intelligence to the smart gird. Precise electricity prediction allows a service provided in resource planning and also controlling actions for the demand and supply balancing. The users are beneficial from the smart metering solution by effective interpretation of their energy utilization, and labelling them to efficiently handle the utilization cost. With this motivation, the paper presents intelligent energy consumption analytics using smart metering data (ECA-SMD) model to determine the utilization of energy. The presented ECA-SMD model involves three major processes namely data pre-processing, feature extraction, classification, and parameter optimization. The presented ECA-SMD model uses Extreme Learning Machine (ELM) based classification to determine the optimum class labels. Besides, shell game optimization (SGO) algorithm is applied for tuning the parameters involved in the ELM and boosts the classification efficiency. The efficacy of the ECA-SMD model is validated using an extensive set of smart metering data and the results are investigated based on accuracy and mean square error (MSE). The proposed model exhibited supremacy with the maximum accuracy of 65.917 % and minimum MSE of 0.096

Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression

Deep neural networks are proposed for short-term natural gas load forecasting. Deep learning has proven to be a powerful tool for many classification problems seeing significant use in machine learning fields such as image recognition and speech processing. We provide an overview of natural gas forecasting. Next, the deep learning method, contrastive divergence is explained. We compare our proposed deep neural network method to a linear regression model and a traditional artificial neural network on 62 operating areas, each of which has at least 10 years of data. The proposed deep network outperforms traditional artificial neural networks by 9.83% weighted mean absolute percent error (WMAPE)

A DEEP LEARNING MODEL FOR ELECTRICITY DEMAND FORECASTING BASED ON A TROPICAL DATA

Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to com¬puter vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load fore¬casting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem

Peak Forecasting for Battery-based Energy Optimizations in Campus Microgrids

Battery-based energy storage has emerged as an enabling technology for a variety of grid energy optimizations, such as peak shaving and cost arbitrage. A key component of battery-driven peak shaving optimizations is peak forecasting, which predicts the hours of the day that see the greatest demand. While there has been significant prior work on load forecasting, we argue that the problem of predicting periods where the demand peaks for individual consumers or micro-grids is more challenging than forecasting load at a grid scale. We propose a new model for peak forecasting, based on deep learning, that predicts the k hours of each day with the highest and lowest demand. We evaluate our approach using a two year trace from a real micro-grid of 156 buildings and show that it outperforms the state of the art load forecasting techniques adapted for peak predictions by 11-32%. When used for battery-based peak shaving, our model yields annual savings of $496,320 for a 4 MWhr battery for this micro-grid.Comment: 5 pages. 4 figures, This paper will appear in the Proceedings of ACM International Conference on Future Energy Systems (e-Energy'20), June 202

AN OVERVIEW OF DEEP LEARNING TECHNIQUES FOR SHORT-TERM ELECTRICITY LOAD FORECASTING

This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems