Thesis Open Access

# FUZZY LOGIC BASED DEMAND SIDE MANAGEMENT AT CUSTOMER END SIDE

AMINAT YAHYA NURE

### DataCite XML Export

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<creators>
<creator>
<creatorName>AMINAT YAHYA NURE</creatorName>
<affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
</creator>
</creators>
<titles>
<title>FUZZY LOGIC BASED DEMAND SIDE MANAGEMENT AT CUSTOMER END SIDE</title>
</titles>
<publisher>National Academic Digital Repository of Ethiopia</publisher>
<publicationYear>2019</publicationYear>
<subjects>
<subject>Fuzzy logic, demand side management, forecast, appliance schedule</subject>
</subjects>
<contributors>
<contributor contributorType="Supervisor">
<contributorName>Professor Ramasamy</contributorName>
<affiliation>ADDIS ABABA SCIENCE AND TECHNOLOGY UNIVERSITY</affiliation>
</contributor>
</contributors>
<dates>
<date dateType="Issued">2019-11-18</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="Text">Thesis</resourceType>
<alternateIdentifiers>
</alternateIdentifiers>
<relatedIdentifiers>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;Demand Side Management (DSM) technique encourages the consumers to adjust their energy&lt;br&gt;
usage pattern to get optimized results for achieving the goal of minimizing the electricity&lt;br&gt;
consumption cost. This mechanism provides benefits to both side customer and utility in terms of&lt;br&gt;
bill cost reduction (for the customer) and ensure grid stability (for the provider). To regulate the&lt;br&gt;
increasing energy demand extensive research is being carried out for possible implementation&lt;br&gt;
of different DSM techniques.&lt;br&gt;
In this thesis, an Energy Management System (EMS) at customer end side through design of&lt;br&gt;
fuzzy logic based demand side management model is proposed for demand forecasting and load&lt;br&gt;
scheduling. The proposed system consists of two fuzzy logic controllers, the 1st controller is&lt;br&gt;
applicable for the short term energy demand forecasting for the next day 24hours and the 2nd&lt;br&gt;
controller is used to schedule the home appliances based on the forecasted demand. The input&lt;br&gt;
variables for the forecasting controllers are previous demand, current demand, day type (work&lt;br&gt;
day or weekend) and time period of the day. Depending on these parameters it forecasts the next&lt;br&gt;
day demand which is considered as its output. Then this output (forecasted demand) will feed to&lt;br&gt;
the second fis (scheduler) controller to schedule the specified appliances.&lt;br&gt;
Finally the fuzzy logic based predictive controller is developed and implemented using&lt;br&gt;
appropriate membership function in order to forecast the next day&amp;rsquo;s demand. And the result of&lt;br&gt;
this model shows that forecasted demand is highly affected by particular time period, previous&lt;br&gt;
demand, current demand and day type .The displayed result indicates that higher demand is&lt;br&gt;
occurred at peak hours and low demand at off-peak hours. Depend on the forecasted demand&lt;br&gt;
fuzzy logic based load scheduling controller is developed and implemented. The result of the&lt;br&gt;
scheduler shows at higher forecasted demand most of appliances are in off condition except&lt;br&gt;
continuously usable loads .And all home loads are in on condition at low and medium forecasted&lt;br&gt;
demand. From this we can conclude that everybody can manage his power consumption by&lt;br&gt;
shifting their usage from peak hour to off peak hour by knowing the demand of energy for the&lt;br&gt;
next day with the help of the model .and the model appropriately schedules the home loads.&lt;br&gt;
&amp;nbsp;&lt;/p&gt;</description>
</descriptions>
</resource>

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