Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting

<table class="table-group" id="tab3"><tr><td><table class="table"><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr class="thead"><td align="left">Criterion </td><td align="center"> Persistence </td><td align="center"> BI best </td><td align="center"> BI average </td><td align="center"> CFD best </td><td align="center"> CFD average </td></tr><tr><td class="thead-hr" colspan="6"><hr/></td></tr><tr><td align="left">RMSE </td><td align="center"> 2.3554</td><td align="center"> 1.7679 </td><td align="center"> 1.7895 </td><td align="center"> 1.8138 </td><td align="center"><span style="width: 37.1977ptpx;"><svg height="8.84376pt" id="M202" style="vertical-align:-0.2401896pt" version="1.1" viewbox="-0.0498162 -8.60357 37.1977 8.84376" width="37.1977pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M409 0V34C329 39 322 56 322 125V635C234 611 160 593 94 583V554L145 551C186 547 190 541 190 488V125C190 54 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166 -12C226 -11 297 15 354 56C424 105 455 164 455 226C455 312 382 394 254 394C228 394 175 380 125 362L152 515Z" id="g13-51"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g></svg></span></td></tr><tr><td align="left">MAE </td><td align="center"> 1.8693</td><td align="center"> 1.3701 </td><td align="center"> 1.3589 </td><td align="center"> 1.4317 </td><td align="center"><span style="width: 37.1977ptpx;"><svg height="8.84376pt" id="M203" style="vertical-align:-0.2401896pt" version="1.1" viewbox="-0.0498162 -8.60357 37.1977 8.84376" width="37.1977pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M409 0V34C329 39 322 56 322 125V635C234 611 160 593 94 583V554L145 551C186 547 190 541 190 488V125C190 54 181 39 102 34V0H409Z" id="g13-47"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,6.774,0)"><path d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z" id="g113-47"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="228" vert-adv-y="228"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,9.863,0)"><path d="M306 382C339 403 362 417 379 433C398 450 411 473 411 500C411 578 352 635 248 635H247C181 635 134 604 106 573L56 506L82 482C117 529 151 557 201 557C246 557 287 531 287 466C287 404 228 364 143 336L150 302C165 306 186 309 204 309C258 309 324 275 324 179C324 85 285 44 235 44C175 44 128 88 106 108C92 118 80 116 69 106C56 97 39 79 39 61C39 47 46 34 63 21C83 5 123 -12 174 -12C236 -12 454 58 454 222C454 297 403 362 306 380V382Z" id="g13-49"></path><glyph.data ascent="1024" descent="-360" horiz-adv-x="500" vert-adv-y="500"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,16.637,0)"><path d="M486 174V231H396V629H324C229 512 123 368 15 215V174H269V116C269 55 264 41 179 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Wind speed forecast assessment for various cases using NARX(<svg height="10.6388pt" id="M199" style="vertical-align:-3.571919pt" version="1.1" viewbox="-0.0498162 -7.06688 20.2125 10.6388" width="20.2125pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z" id="g113-113"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="593" vert-adv-y="593"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,8.035,0)"><path d="M95 130C70 130 46 113 46 88C46 72 54 64 59 64C93 55 121 33 121 -3C121 -41 93 -68 44 -88L55 -117C117 -98 186 -56 186 22C186 91 131 130 95 130Z" id="g113-45"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="228" vert-adv-y="228"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,13.395,0)"><path d="M474 429L457 433C435 440 389 448 367 448C348 448 323 446 309 443C266 434 195 406 148 366C78 307 23 210 23 101C23 35 55 -12 92 -12C118 -12 146 1 196 35C247 70 311 130 346 173H348L281 -148C268 -211 256 -221 208 -229L191 -232L187 -257L433 -245L437 -219L411 -216C357 -210 350 -205 362 -140L427 207C447 315 461 381 474 429ZM387 387C379 337 363 262 355 236C318 180 201 57 142 57C126 57 112 81 112 128C112 205 150 321 220 376C244 395 280 403 312 403C345 403 370 396 387 387Z" id="g113-114"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="491" vert-adv-y="491"></glyph.data></g></svg>) ANNs with <svg height="12.2707pt" id="M200" style="vertical-align:-3.57194pt" version="1.1" viewbox="-0.0498162 -8.69876 30.244 12.2707" width="30.244pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M570 304C570 398 525 448 414 448C385 448 343 445 312 434L329 511L321 518C297 504 262 482 244 460L233 411C195 397 159 381 128 358L135 332C160 347 189 360 224 373L111 -147C97 -210 84 -218 17 -231L13 -257L254 -247L259 -218L233 -216C183 -212 177 -202 189 -142L218 -1C238 -10 266 -12 283 -12C351 3 429 48 483 105C543 168 570 242 570 304ZM482 289C482 161 380 33 304 33C278 33 248 51 233 69L303 396C326 400 352 403 369 403C428 403 482 380 482 289Z" id="g113-113"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="593" vert-adv-y="593"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,11.82,0)"><path d="M512 230V281L75 514V456L453 256V254L75 55V-3L512 230Z" id="g117-92"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="587" vert-adv-y="587"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,23.558,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g></svg> and <svg height="12.2707pt" id="M201" style="vertical-align:-3.57194pt" version="1.1" viewbox="-0.0498162 -8.69876 28.8569 12.2707" width="28.8569pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.0135,0,0,-0.0135,0,0)"><path d="M474 429L457 433C435 440 389 448 367 448C348 448 323 446 309 443C266 434 195 406 148 366C78 307 23 210 23 101C23 35 55 -12 92 -12C118 -12 146 1 196 35C247 70 311 130 346 173H348L281 -148C268 -211 256 -221 208 -229L191 -232L187 -257L433 -245L437 -219L411 -216C357 -210 350 -205 362 -140L427 207C447 315 461 381 474 429ZM387 387C379 337 363 262 355 236C318 180 201 57 142 57C126 57 112 81 112 128C112 205 150 321 220 376C244 395 280 403 312 403C345 403 370 396 387 387Z" id="g113-114"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="491" vert-adv-y="491"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,10.437,0)"><path d="M512 230V281L75 514V456L453 256V254L75 55V-3L512 230Z" id="g117-92"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="587" vert-adv-y="587"></glyph.data></g><g transform="matrix(.0135,0,0,-0.0135,22.175,0)"><path d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z" id="g113-49"></path><glyph.data ascent="3473" descent="-2876" horiz-adv-x="480" vert-adv-y="480"></glyph.data></g></svg>.

International Scholarly Research Notices

tab3

Table 3

Table 3: Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting 