Forecasting Strategies for Predicting Peak Electric Load Days

Academic institutions spend thousands of dollars every month on their electric power consumption. Some of these institutions follow a demand charges pricing structure; here the amount a customer pays to the utility is decided based on the total energy consumed during the month, with an additional charge based on the highest average power load required by the customer over a moving window of time as decided by the utility. Therefore, it is crucial for these institutions to minimize the time periods where a high amount of electric load is demanded over a short duration of time. In order to reduce the peak loads and have more uniform energy consumption, it is imperative to predict when these peaks occur, so that appropriate mitigation strategies can be developed. The research work presented in this thesis has been conducted for Rochester Institute of Technology (RIT), where the demand charges are decided based on a 15 minute sliding window panned over the entire month. This case study makes use of different statistical and machine learning algorithms to develop a forecasting strategy for predicting the peak electric load days of the month. The proposed strategy was tested for a whole year starting May 2015 to April 2016 during which a total of 57 peak days were observed. The model predicted a total of 74 peak days during this period, 40 of these cases were true positives, hence achieving an accuracy level of 70 percent. The results obtained with the proposed forecasting strategy are promising and demonstrate an annual savings potential worth about $80,000 for a single submeter of RIT

A Customer Agnostic Machine Learning Based Peak Electric Load Days Forecasting Methodology for Consumers With and Without Renewable Electricity Generation

The adoption of electricity generation from renewable sources, as well as the push for a speedy electrification of sectors such as transportation and buildings, makes peak electric load management an essential aspect to ensure the electric grid’s reliability and safety. Utilities have established peak load charges that can amount to up to 70% of electricity costs to transfer the financial burden of managing these loads to the consumers. These pricing schemes have created a need for efficient peak electric load management strategies that consumers can implement in order to reduce the financial impact of this type of load. Research has shown that the impact of peak load charges can be reduced by acting on the intelligence provided by peak electric load days (PELDs) forecasts. Unfortunately, published PELDs forecasting methodologies have not addressed the increasing number of facilities adopting behind the meter renewable electricity generation. The presence of this type of intermittent generation adds substantial complexity and other challenges to the PELDs forecasting process. The work reported in this dissertation is organized in terms of its three main contributions to the body of knowledge and to society. First, the development and testing of a first of its kind PELDs forecasting methodology able to accurately predict upcoming PELDs for a consumer regardless of the presence or absence of renewable electricity generation. Experimental results showed that 93% and 90% of potential savings (approximately US$142,129.01 and US$ 123,100.74) could be achieved by a consumer with and a consumer without behind the meter solar generation respectively. The second contribution is the development and testing of a novel methodology that allows virtually any type of consumer to determine an efficient electricity demand threshold value before the start of a billing period. This threshold value allows consumers to proactively trigger demand response actions and reduce peak demand charges without receiving any type of signal or information from the utility. Experimental results showed 65% to 82% of total potential demand charge reductions achieved during a year for three different consumers: residential, industrial, and educational with solar generation. These results translate to US$149.09, US$ 23,290.00, and US$ 107,610.00 in demand charges savings a year respectively. As a third contribution, we present experimental results that show how the implementation of machine learning based ensemble classification techniques improves the PELDs forecasting methodology’s performance beyond previously published ensemble techniques for three different consumers

Peak Electric Load Relief in Northern Manhattan: An Exploratory Data Analysis

The aphorism “Think globally, act locally,” attributed to René Dubos, reflects the vision that the solution to global environmental problems must begin with efforts within our communities. PlaNYC 2030, the New York City sustainability plan, is the starting point for this study. Results include (a) a case study based on the City College of New York (CCNY) energy audit, in which we model the impacts of green roofs on campus energy demand and (b) a case study of energy use at the neighborhood scale. We find that reducing the urban heat island effect can reduce building cooling requirements, peak electricity loads stress on the local electricity grid and improve urban livability

Performance Testing of Radiant Barriers

TVA has conducted a study to determine the effects of radiant barriers (RBI (i.e., material with a low emissivity surface facing an air space), when used with fiberglass, on attic heat transfer during summer and winter. This study employed five small test cells exposed to ambient conditions and having attics with gable and soffit vents. Three different RB configurations were tested and compared to the non-RR configuration. Heat flux transducers determined the heat transfer between the attic and conditioned space. The results showed that all RB con figurations significantly reduced heat gain through the ceiling during the summer. Reductions in heat gain during daylight and peak electric load hours were especially attractive. Roof temperatures for the RB configurations were only slightly higher than for the non-RB case. Heat transfer reductions for the RB configurations in the winter were smaller than those for the summer but were still significant in many, but not all, situations. Savings during night and peak electric load hours were especially attractive

Solar cooling: a case study

Throughout the years various methods for heat prevention and indoor temperatures control in the summer have been used. The alternative cooling strategies are based on various passive and low energy cooling technologies for protection of the buildings via design measures or special components to moderate the thermal gains, or to reject the excess heat to the ambient environment. All these techniques aim to reduce summer cooling loads and electricity demand for air conditioning. During the summer the demand for electricity increases because of the extensive use of heating ventilation air conditioning (HVAC) systems, which increase the peak electric load, causing major problems in the electric supply. The energy shortage is worse during ‘dry’ years because of the inability of the hydroelectric power stations to function and cover part of the peak load. The use of solar energy to drive cooling cycles for space conditioning of most buildings is an attractive concept, since the cooling load coincides generally with solar energy availability and therefore cooling requirements of a building are roughly in phase with the solar incidence. Solar cooling systems have the advantage of using absolutely harmless working fluids such as water, or solutions of certain salts. They are energy efficient and environmentally safe. The purpose of this paper is to describe a Solar Cooling System to be installed on the roof of a building in Rome, the headquarters of the State Monopoly. The medium size power plant is composed of the following components: − Solar Collectors; − Thermal Storage Tank; − Absorption Chiller; The plant design is based on a dynamic simulation in TRNSYS, a dynamic simulation tool used by engineers all over the world to make energy calculations in a transient state

Forecasting Energy Demand & Peak Load Days with the Inclusion of Solar Energy Production

The addition of solar panels to forecasting energy demand and peak energy demand presents an entirely new challenge to a facility. By having to account for the varying energy generation from the solar panels on any given day based on the weather it becomes increasingly difficult to accurately predict energy demand. With renewable energy sources becoming more prevalent, new methods to track peak energy demand are needed to account for the energy provided by renewable sources. We know from previous research that Artificial Neural Networks (ANN) and Auto Regressive Integrated Moving Average (ARIMA) models are both capable of accurately forecasting building demand and peak electric load days without the presence of solar panels. The goal of this research was to take three different approaches for both the ANN model and the ARIMA model to find the most accurate method for forecasting monthly energy demand and peak load days while considering the varying daily solar energy production. The first approach used was to forecast net demand outright based on relevant historical training data including weather information that would help the models learn how this information affected the overall net demand. The second approach was to forecast the building demand specifically based on the same relevant historical data and then use a random decision tree forest to predict the cluster of day that each day of the month would be in terms of solar production (high, medium with early peak, medium with late peak, low). After the type of day was predicted we would subtract the average solar energy production of the predicted cluster to receive our forecasted net demand for that day. The third approach was similar to the second, but instead of subtracting the average of the cluster we subtracted multiple randomly generated days from that cluster to provide multiple overlapping forecasts. This was specifically used to try and better predict peak load days by testing the hypothesis that if 80% or higher predicted a peak day it would in fact be a peak day. The ANN model outperformed the ARIMA for each approach. Forecasting multiple days was the best of the three approaches. The multiple day ANN forecast had the highest balanced accuracy and sensitivity, the net demand ANN approach was the 2nd most accurate approach and the average solar ANN forecast was the 3rd best approach in terms of balanced accuracy and sensitivity. Based on the outcomes of this study, consumers and institutions such as RIT will be better able to predict peak usage days and use preventative measures to save money by reducing their energy intake on those predicted days. Another benefit will be that energy distribution companies will be able to accurately predict the amount of energy customers with personal solar panels will need in addition to the solar energy they are using. This will allow a greater level of reliability from the providers. Being able to accurately forecast energy demand with the presence of solar energy is going to be critical with the ever-increasing usage of renewable energy

Integrated Thermal Energy Storage System For Air-conditioners With Phase-Change Composites

Thermal energy storage (TES) is a promising solution to store and dispatch energy and shave peak electric load, reducing the operational cost of HVAC systems. We present results of a TES system using phase-change materials (PCM) integrated with an air conditioner. The proposed system uses an organic PCM (tetradecane) encapsulated within compressed expanded natural graphite foams referred to as phase-change composite. The graphite foam encapsulates the PCM eliminating the need for expensive storage vessels, reduces installation costs, and provides higher thermal conductivity that can lead to faster charge/discharge rates. Two serpentines, multi-pass circuits, operating as a heat source and a sink, exchange heat to and from the phase-change composite. These two circuits are embedded in multiple slabs of this material. The “charge” circuit contains the refrigerant that is directly coupled to a vapor compression system, and the “discharge” circuit removes heat from an airstream and releases it into the PCM composite through a water-glycol liquid coupling. This configuration allows for multiple modes of operation depending on the state of charge of the thermal energy storage module, the building air-conditioning load, and the current electricity and demand charges. This flexible operation allows variable air volume capacity control without the need to have a variable capacity refrigeration system. We developed a 21 kW-hr (6 RT-hr) prototype TES system coupled with a commercial air-conditioner to characterize the component-and system-level performance

Municipal District Heating and Cooling Co-generation System Feasibility Research

In summer absorption refrigerating machines provide cold water using excess heat from municipal thermoelectric power plant through district heating pipelines, which reduces peak electric load from electricity networks in summer. The paper simulates annual dynamic load of a real project to calculate the first investments, annual operation cost and LCC (life cycle cost) of the four schemes, which are electric chillers, electric chillers with ice-storage system, absorption refrigerating machines using excess heat from power plant and absorption refrigerating machines using excess heat from power plant along with ice-storage system. On the basis of the results, the paper analyzes the prospect of the absorption refrigeration using municipal excess heat, as well as the reasonable heat price, which provides a theoretical basis for municipal heating and cooling co-generation development

Susceptibility Ranking of Electrical Feeders: A Case Study

Ranking problems arise in a wide range of real world applications where an ordering on a set of examples is preferred to a classification model. These applications include collaborative filtering, information retrieval and ranking components of a system by susceptibility to failure. In this paper, we present an ongoing project to rank the feeder cables of a major metropolitan area's electrical grid according to their susceptibility to outages. We describe our framework and the application of machine learning ranking methods, using scores from Support Vector Machines (SVM), RankBoost and Martingale Boosting. Finally, we present our experimental results and the lessons learned from this challenging real-world application