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

Discovering And Labelling Of Temporal Granularity Patterns In Electric Power Demand With A Brazilian Case Study

Clustering is commonly used to group data in order to represent the behaviour of a system as accurately as possible by obtaining patterns and profiles. In this paper, clustering is applied with partitioning-clustering techniques, specifically, Partitioning around Medoids (PAM) to analyse load curves from a city of South-eastern Brazil in São Paulo state. A top-down approach in time granularity is performed to detect and to label profiles which could be affected by seasonal trends and daily/hourly time blocks. Time-granularity patterns are useful to support the improvement of activities related to distribution, transmission and scheduling of energy supply. Results indicated four main patterns which were post-processed in hourly blocks by using shades of grey to help final-user to understand demand thresholds according to the meaning of dark grey, light grey and white colours. A particular and different behaviour of load curve was identified for the studied city if it is compared to the classical behaviour of urban cities.36357559

Analysing the Residential Electricity Consumption using Smart Meter

A massive amount of electricity usage may be accessed on an everyday and hourly basis due to the advancement of smart power measuring technology. Electricity demand management and utility load management are made easier by energy usage forecasts. The majority of earlier studies have concentrated on the power consumption of business clients or residential buildings, or they have experimented with individual household electricity usage using behavioral and occupant sensor information. This study used smart meters to examine energy usage at a single household level to enhance residential energy services and gather knowledge for developing demand response strategies.The power usage of various appliances in a single household is estimated, by utilizing Autoregressive Integrated Moving Average (ARIMA) modeling technique, which is applied to daily, weekly, and monthly information granularity. To select the household’s energy consumption dataset for this study, a multivariate time-series dataset describing the four-year electricity usage of a household is provided. The use of Exploratory Data Analysis (EDA) is utilizedfor the selection of features and data visualization. The correlation coefficients with the daily usage of the household have been computed for the characteristics prepared for the forecast. The top three major determinants with the top three positive significance are "temperature," "hour of the day," and "peak index." A single household's usage is inversely related to the variables having negative coefficients. It should be noticed that the correlations among a household's attributes with usage vary from one another. Finally, the power prediction is analyzed in a single household

Day-Ahead Solar Resource Prediction Method Using Weather Forecasts for Peak Shaving

Due to recent concerns about energy sustainability, solar power is becoming more prevalent in distributed power generation. There are still obstacles which need to be addressed before solar power can be provided at the level of reliability that utilities require. Some of these issues can be mitigated with strategic use of energy storage. In the case of load shifting, energy storage can be used to supply solar energy during a time of day when utility customer\u27s demand is highest, thus providing partial peak load burden relief or peak shaving. Because solar resource availability is intermittent due to clouds and other atmospheric factors, charge/discharge planning must take weather into consideration. Many inter-day and intra-day solar resource prediction methods have been developed to aid in rm (high-reliability) resource establishment and peak-shaving through various methods and data sources with different levels of complexity. The purpose of this study was to investigate the use of readily-available, day-ahead National Weather Service (NWS) forecasts to develop a PV resource prediction. Using past day-ahead NWS weather forecasts and historical performance data from the Prosperity Energy Storage Project near Mesa del Sol in Albuquerque, New Mexico, several correlations were created based on regression analysis and optimized for minimal Root Mean Square (RMS) error for daily insolation prediction. Though some other methods such as the National Digital Forecast Database (NDFD) and Global Forecast System (GFS) exhibit greater accuracy, this method could prove to be a relatively simple means of planning the use of energy storage for peak-shaving or arbitrage. Additionally, given appropriate considerations for prediction uncertainty one could establish a rm resource to meet customer demand

Short-Term Load Forecasting Using AMI Data

Accurate short-term load forecasting is essential for efficient operation of the power sector. Predicting load at a fine granularity such as individual households or buildings is challenging due to higher volatility and uncertainty in the load. In aggregate loads such as at grids level, the inherent stochasticity and fluctuations are averaged-out, the problem becomes substantially easier. We propose an approach for short-term load forecasting at individual consumers (households) level, called Forecasting using Matrix Factorization (FMF). FMF does not use any consumers' demographic or activity patterns information. Therefore, it can be applied to any locality with the readily available smart meters and weather data. We perform extensive experiments on three benchmark datasets and demonstrate that FMF significantly outperforms the computationally expensive state-of-the-art methods for this problem. We achieve up to 26.5% and 24.4 % improvement in RMSE over Regression Tree and Support Vector Machine, respectively and up to 36% and 73.2% improvement in MAPE over Random Forest and Long Short-Term Memory neural network, respectively